Using psychometrics and Signal Detection Theory to investigate tone perception in 490 native speakers of Kam (Dong)

Supplementary Materials (full analysis report)

Links to the main figures and tables referenced in the main paper:

• Figures:
  • Figure S1. Distribution of gender.
  • Figure S2. Distribution of age.
  • Figure S4. Distribution of years of musical traning.
  • Figures S5-S6. Distribution of years of education by gender and by location.
  • Figure S7. Predictive plot of the linear regression of education on gender, age and their interaction.
  • Figures S9-S11. PCA of working memory.
  • Figures S12-S14. EFA of working memory.
  • Figure S15. CFA of working memory.
  • Figure S16. Normalized working memory by gender and age.
  • Figure S17. Number of correct responses on the tone task (original dataset).
  • Figures S18-S21. Correlation matrix, its histogram, hierarchical clustering, and average correlations for the tone task (original dataset).
  • Figure S24. Guttman errors for the tone task (original dataset).
  • Figures S25-S27. PCA of the tone task (original dataset).
  • Figures S28-S30. EFA of the tone task (original dataset).
  • Figures S31-S35. Correlation matrix, its histogram, hierarchical clustering, and average correlations for the tone task (recoded dataset).
  • Figures S36-S38. PCA of the tone task (recoded dataset).
  • Figures S39-S41. EFA of the tone task (recoded dataset).
  • Figures S42-S43. Histograms and correlations heatmaps (with clustering) for pcr on the three datasets.
  • Figures S46-S47. Histograms and correlations heatmaps (with clustering) for SDT-deroved measures on the three datasets.
  • Figure S55. K-means clustering for the tone task (original dataset).
  • Figure S56. % different vs % same for the tone task clusters.
  • Figures S57-S58. Correlations heatmaps (with clustering) for all measures of interest.
  • Figure S60. Mediation gender → education → working memory.
  • Figure S61. Mediation age → education → working memory.
  • Figures S62-S64. Multiple regression of pcr (recoded dataset).
  • Figure S65. Mediation gender → education → tone (recoded dataset).
  • Figure S66. Mediation age → education → tone (recoded dataset).
  • Figure S67. Mediation location → education → tone (recoded dataset).
  • Figure S68. Mediation education, age, gender → working memory → tone (recoded dataset).
  • Figures S70-S77. Corresponding analyses for d’ (recoded dataset).
  • Figures S94-S99. Comparing the complex path models for % correct responses and d’ on the original, recoded and removed datasets.
• Tables:
  • Table S1. The working memory trials.
  • Tables S5-S6. Tone task stimuli.
  • Table S7. Frequencies of correct responses on the tone task (original dataset).
  • Tables S9-S10. Item homogeneities for the tone task (original dataset).
  • Table S11. aisp for the tone task (original dataset).
  • Table S12. Frequencies of correct responses on the tone task (recoded dataset).
  • Tables S13-S14. Item homogeneities for the tone task (recoded dataset).
  • Table S15. aisp for the tone task (recoded dataset).
  • Tables S16-S17. Item homogeneities for the tone task (reduced dataset).
  • Table S19. aisp for the tone task (reduced dataset).
  • Table S24. W and non-W item correlations.
  • Table S26. Comparisons between measures and datasets.
  • Table S27. Examples of response patterns detectable by SDT but not by % correct responses.

1 Introduction

This document (irrespective of its format, most probably HTML or PDF) resulted from compiling the corresponding Rmarkdown script and contains all the results and plots supporting the paper (its Supplementary Materials). The primary data are available upon request from the corresponding author (Manxiang Wu manxiangwu1022@163.com), as instructed in the paper, but all the code needed to reproduce this document are available in the GitHub repository https://github.com/ddediu/tone_ax_dong.

Please note that this Rmarkdown script caches some very expensive computations in the ./cached_results directory, but it can be manually forced to recompute everything (setting the variable FORCE_COMPUTE_ALL to TRUE) and it also forces a full recomputation if any of the input files (in the directory ./input_files) was changed, deleted or added (or if this is the first time this is run). However, it is highly recommended to not run such a full recomputation during the knitting of the Rmarkdown, but instead in a “normal” R session using, for example, the “Run ▾” → “Run all” menu in RStudio (knitting the whole thing seems to generate crashes due to memory issues). Also, this full recomputation should be done on a powerful machine (with at least 32Gb RAM and a 4 cores CPU) running Linux or macOS (to fully use multicore parallelism; moreover, this script was not tested on Windows) and it may take a while (it takes about 3 hours on an AMD Ryzen 7 3700X with 64Gb RAM), but the subsequent knitting or small changes can be run on a “normal” machine as the expensive results are cached for later use.

1.1 Typographic conventions

This document uses the following font and color conventions:

• regular text is rendered as “regular text”;
• emphasis is represented using italic text, bold text or bold+italic text;
• software or programming concepts (e.g., applications, packages or function names) are represented using fixed font text;
• section heads use specific font sizes and are numbered;
• hyperlinks to sections of this document and to external resources on the web are represented as link to the Introduction or link to R project’s website, and can be clicked to navigate there;
• notes are represented as numbered superscripts¹ which can be clicked to go to the note’s text;
• captions use colored font, are numbered, and are placed below the corresponding figure and above the corresponding table;
• raw output, as produced by various R functions and expressions, is shown using fixed font text in clearly marked boxes;

1.2 Software and hardware info

The full information about the version of R (R Core Team, 2023), the packages and the hardware and software platform used to obtain this document are given in the Section Session information at the end of this document².

2 The data

2.1 The population and language

We collected data from native speakers of a Southern dialect of Kam, described in detail in (Wu, 2018), probably Glottolog sout2741, which is characterized by a very complex tone system (see Wu, 2018, pp. 28–36) with 10 phonemic (i.e., contrastive) tones realized as 15 phonetic tones (and effected by various tone sandhi rules and language contact-induced ongoing changes). As expected, WALS assigns a “Complex tone system” to this variety (see “Chapter 13A” for Language Dong (Southern)); unfortunately, neither PHOIBLE not LAPSyD seem to contain any information about it.

In total, we collected usable data from 492 unique participants, from which we further excluded 2 participants who reported hearing problems (no participant reported brain or cognitive impairment), leaving a total of 490 participants in the sample.

Concerning self-declared gender, there are 331 (67.6%) self-declared females and 159 (32.4%) self-declared males:

Figure S1. Distribution of gender in the sample. Figure generated using R version 4.3.3 (2024-02-29)

At the time of data collection, the age of the participants was distributed between 15 and 72 years, with a mean of 40.4 (and median 44) and standard deviation of 12.6 (and interquartile range, IQR, 18):

Figure S2. Distribution of age overall (thick black curve) and by gender (colored transparent curves) in the sample. Figure generated using R version 4.3.3 (2024-02-29)

It can be seen that the sample has 2.1 times more females than males (the χ² test against the expected 50%:50% distribution is highly significant: χ²(1) = 60.4, p=7.84×10^-15), but the ages are distributed in similar ways between the two genders, with a bi-modal distribution suggesting two age groups: one composed of adolescents and young adults (centered around 20 years of age and ranging between the minimum of 15 and about 30 years old) and the other (“adults”) centered around late 40s.

We used the location as a proxy for any relevant socio-linguistic dimensions of variation, and we ended up with participants from 5 locations:

B	A	C	D	E
237	227	1	1	1

The vast majority of participants comes from two neighboring locations A and B (about 8km apart), speaking very similar dialects of Kam (Manxiang Wu, pc); therefore we collapsed the remaining locations into an “other” category. Please note that for 23 (4.7%) participants this information is missing.

We also were able to retrieve some information concerning familial relationships for 91 (18.6%) participants, grouped in 28 nuclear families across a maximum of 3 generations: generation 0 comprises the youngest members, 1 is their parents’ generation, and 2 is that of their grandparent’s generation:

Figure S3. Distribution of the participants with information about family by generation: 0=youngest (black), 1=their parents (gray) and 2=their grandparents (light gray). The families are identified with an arbitrary unique numerical ID. Figure generated using R version 4.3.3 (2024-02-29)

It can be seen that there is only one participant in generation 2, so we collapsed generations 1 and 2, resulting into a binary split into a “young” and an “older” generation.

young	older
72	19

2.2 The covariates

We considered three covariates here (as age has already been covered above, we focus on the remaining two).

2.2.1 Years of musical traning

This variable (music_years) is self-declared and is distributed as follows:

Figure S4. Distribution of music_years overall (thick black curve) and by gender (colored transparent curves) in the sample. Figure generated using R version 4.3.3 (2024-02-29)

It is clear that in our sample it does not capture any interesting pattern of inter-individual variation, so we will ignore it in the following analyses.

2.2.2 Years of formal education

This variable (education_years) is self-declared and is distributed as follows:

Figure S5. Distribution of education_yearsby gender in the sample. Figure generated using R version 4.3.3 (2024-02-29)

Figure S6. Distribution of education_years by location in the sample. NA means that the location information was not available. Figure generated using R version 4.3.3 (2024-02-29)

Overall, gender makes a significant difference (t(394.2)=-6.4, p=3.5×10^-10; Mann-Whitney W=1.75565^{4}, p=1.75×10^-9), with males having 2.35 years of formal education more than females on average (i.e., 7.64 for males vs 5.29 for females).

Focusing on the two locations that comprise the majority of the participants, location A has significantly higher educational levels that location B (t(461.9)=4, p=8.05×10^-5; Mann-Whitney W=3.25335^{4}, p=8.24×10^-5), with the participants from location A having 1.53 years of formal education more than the participants from B on average (i.e., 6.67 vs 5.14).

We performed the linear regression of education_years on age, gender and their interaction, and we found that this model behaves well (diagnostic plots not shown), and that it explains adjusted R² = 48.7% of the variance, and that all terms are significant: age has a highly significant negative main effect (β = -0.264, p = 7.22×10^-60), gender has a very large and highly significant main effect with the males having less years of education than females (β = -3.758, p = 8.23×10^-5), but there is a highly significant interaction between the two (β = 0.138, p = 2.92×10^-9) that offsets the main negative effect of gender into an advantage for males versus the females:

Figure S7. Predictive plot of the linear regression of education_years on gender, age and their interaction. Figure generated using R version 4.3.3 (2024-02-29)

2.3 The measures

2.3.1 The working memory task

The working memory task consists of 15 trials; in each trial a sequence of colors (each color appears only once in a trial) are shown to the participant and the participant has to reproduce the colors in the correct order, the score representing the number of colors reproduced in the correct position. For example, trial 1 shows “red”, “green” and “blue”, and if a participant reproduces “yellow”, “green”, “red”, her score is 1 (from “green”). The trials are the same across participants and vary from 3 to 7 colors, as show below:

Trial	Length	Color 1	Color 2	Color 3	Color 4	Color 5	Color 6	Color 7
1	3	red	green	blue
2	3	black	purple	yellow
3	3	gray	green	black
4	4	red	blue	purple	gray
5	4	yellow	black	green	blue
6	4	green	red	black	yellow
7	5	blue	black	gray	yellow	red
8	5	gray	purple	yellow	green	blue
9	5	black	red	blue	gray	green
10	6	green	black	purple	blue	gray	yellow
11	6	yellow	purple	black	red	green	gray
12	6	gray	purple	blue	red	green	yellow
13	7	red	green	blue	black	purple	yellow	gray
14	7	blue	gray	black	green	red	purple	yellow
15	7	yellow	red	blue	green	gray	black	purple

Table S1. The working memory trials showing their length and the color sequence. Each color may appear at most once in any give trial but can repeat across trials. The trials are fixed across all participants.

The distribution of scores across participants by trial is:

Figure S8. Distribution of the working memory scores across the trials by gender (showing the actual counts with the females stacked on top of the males). Figure generated using R version 4.3.3 (2024-02-29)

gender makes a significant difference (t(4798.1)=-4.3, p=2.04×10^-5; Mann-Whitney W=5.521666^{6}, p=1.88×10^-6), with males scoring slightly higher across trials than females by 0.17 points on average (i.e., 2.29 for males vs 2.11 for females).

The three trials with the same length visually seem to have similar behaviors, but there are nevertheless significant differences between them:

Length	Trials	ANOVA by trial	Signif. pairwise diffs.
3	01, 02, 03	F(2, 1467)=13.54, p=1.5×10-6	trial 01 is easier
4	04, 05, 06	F(2, 1467)=0.25, p=0.782	all trials are similar
5	07, 08, 09	F(2, 1467)=13.18, p=2.12×10-6	trial 09 is easier
6	10, 11, 12	F(2, 1467)=3.76, p=0.023	trial 12 is easier than trial 10
7	13, 14, 15	F(2, 1467)=13.05, p=2.41×10-6	trial 14 is harder

Table S2. Comparing the trials of the same length using one-way ANOVA with posthoc Tukey pairwise comparisons (details not shown here but summarized in the last column).

We conducted both a Principal Component Analysis (PCA) and an Exploratory Factor Analysis (EFA) on all trials together, and we found that there seems to be a single factor.

For PCA, PC₁ explains 40.2% of the variance, followed by PC₂ which explains only 6.1%, suggesting that all trials load on a single latent variable:

Figure S9. Screeplot of the PCA of all the working memory trials together. Figure generated using R version 4.3.3 (2024-02-29)

Figure S10. Loading of the working memory trials on the first 2 PCs. Figure generated using R version 4.3.3 (2024-02-29)

Figure S11. The participants plotted on the first 2 PCs, colored by their their qualities of representation (cos2). Figure generated using R version 4.3.3 (2024-02-29)

For EFA, all the preliminary tests suggest that factor analysis is appropriate (Kaiser-Meyer-Olkin = 0.95 > 0.60; Bartlett’s test is significant: χ²(105)=2297.1, p=0; and det(cor(data))=0.0086 > 0) and all the recommended methods for finding the appropriate number of factors suggest that 1 factor is enough:

Figure S12. Screeplot of the observed, simulated and randomized data with 1 standard deviation error bars (as generated by fa.parallel()). Figure generated using R version 4.3.3 (2024-02-29)

Figure S13. Number of factors as suggested by the VSS criterion (top left), the complexity of the solution (top right), BIC (bottom left) and Root Mean Residual (bottom right), as implemented by nfactors(). Figure generated using R version 4.3.3 (2024-02-29)

Even if this 1-factor model does not seem to formally be sufficient to explain all of the variance in the data (about 35.8% of the variance explained, but χ²(90)=131.0, p=0.0031 < 0.05), the loadings are very similar:

Figure S14. Loadings of the variables in the 1 factor model. Figure generated using R version 4.3.3 (2024-02-29)

We also implemented a Confirmatory Factor Analysis (CFA) with all trails loading on a single latent wm variable, and we found that while the model formally does not fit the data (χ²(133)=90.0, p=0.0022 ≤ 0.05), its fit indices are ok (CFI=0.98, TLI=0.98, RFI=0.93, RMSEA=0.03) and the path coefficients suggest that all trials load in similar ways on the wm latent:

Figure S15. Confirnatory factor analysis (CFA) of the working memory trislas with a single latent factor wm. Figure generated using R version 4.3.3 (2024-02-29)

Given all these, it makes sense to compute a total score from all the trials (i.e., using the same weight of 1.0); we further normalized it between its minimum possible score of 0.0 and its maximum of 3 × (3+4+5+6+7) = 75 (this variable will be denoted as wm_norm).

This variable is distributed as follows:

Figure S16. A: distribution of normalized working memory score (wm_norm) overall (thick black curve) and by gender (colored transparent curves) in the sample. B: relationship between wm_norm and age by gender with linear regression lines (and 95%CIs). C: Relationship between wm_norm and education_years by gender with linear regression lines (and 95%CIs). Figure generated using R version 4.3.3 (2024-02-29)

We performed the linear regression of wm_norm on age, the number of years of formal education (education_years), gender (the reference level being females) and all their interaction, and, following manual simplification, we found that this model behaves well (diagnostic plots not shown), that it explains adjusted R² = 50.6% of the variance, and that age has a highly significant negative effect (β = -0.006, p = 5.62×10^-17), education_years has a highly significant positive effect (β = 0.021, p = 3.95×10^-22), and gender shows a significant difference between males and females, with males having overall smaller score than females (β = -0.032, p = 0.025).

2.4 The tone task

The tone task is a AX task in which the participant is presented, in a given trial, with a pair of syllables that may differ only in tone, and has to decide if the two syllables are the “same” or “different”. For a given tone pair (let’s say “^a” and “^b”), there are two syllables with different segmental content (let’s say, “A” and “B”), resulting in the following four syllables+tone combinations: A^a, A^b, B^a and B^b. With these, we have the following possible trials:

• “same”: A^aA^a, A^bA^b, B^aB^a and B^bB^b – the tones and segmental content of the two syllables are the same (these are denoted as “same_1”, “same_2”, “same_3” and “same_4”),
• “different”: A^aA^b, A^bA^a (the order differs), B^aB^b, B^bB^a (the order differs), resulting in 4 pair × order trials (denoted as “order_1” and “order_2”, and “pair_1” and “pair_2”, respectively).

Each trial was repeated twice and the repose to a given trial was scored as “correct” if the tones were different and the repose was “different”, or if the tones were the same and the response was “same”, and was scored as “incorrect” otherwise. Each participant was presented with a random (unique) order of the trials. Please note that some of the possible trials were not included in the task, as the participants showed a ceiling effects during the pilot study.

We used the 9 (of the 10) phonological tones in the language that occur in unchecked syllables (Wu, 2018), represented here by letters:

Table S3. Representing tones (column names, using the numeric notation with 5 levels) by letters (first row). Please see Wu (2018) and Donohue & Wu (2013) for details about the tones in this language, and, for example, Yip (2002), for a general introduction to tone.

23	33	332	52	452	35	335	32	212
c	h	k	l	p	s	t	v	x

Likewise, in the name of brevity, we denote the segmental content of the syllables using CAPITAL letters, as follows:

Table S4. Uniquely mapping the segmental content of the syllables used onto CAPITAL letters (for notational brevity).

khem	koi	pem	pju	poi	pui	sai	sɐm	sem	seu	səu	som	teu	thɐŋ
A	B	C	D	E	F	G	H	I	J	K	L	M	N

The actual stimuli used are listed below:

Table S5. The 40 stimuli used in the ‘same’ task, showing the segments and tone, orded alphabetically; some are actual words in the language (also maked using bold). Please note that the actual trials present pairs of such identical stimuli.

Syllabe	Segments	Tone	Tone letter	Is real word?	Short name
khem332	khem	332	k	no	Ak
khem335	khem	335	t	no	At
koi33	koi	33	h	no	Bh
koi35	koi	35	s	no	Bs
koi32	koi	32	v	no	Bv
koi212	koi	212	x	no	Bx
pem33	pem	33	h	no	Ch
pem32	pem	32	v	yes	Cv
pju23	pju	23	c	no	Dc
pju33	pju	33	h	no	Dh
pju212	pju	212	x	no	Dx
poi23	poi	23	c	no	Ec
poi52	poi	52	l	no	El
poi212	poi	212	x	no	Ex
pui35	pui	35	s	no	Fs
pui32	pui	32	v	no	Fv
sai335	sai	335	t	no	Gt
sai212	sai	212	x	no	Gx
sɐm35	sɐm	35	s	yes	Hs
sɐm335	sɐm	335	t	no	Ht
sem23	sem	23	c	no	Ic
sem332	sem	332	k	no	Ik
sem452	sem	452	p	no	Ip
sem35	sem	35	s	no	Is
sem335	sem	335	t	no	It
sem32	sem	32	v	no	Iv
sem212	sem	212	x	no	Ix
seu332	seu	332	k	no	Jk
seu212	seu	212	x	no	Jx
səu332	səu	332	k	no	Kk
səu52	səu	52	l	no	Kl
som332	som	332	k	no	Lk
som52	som	52	l	no	Ll
som452	som	452	p	no	Lp
som32	som	32	v	no	Lv
teu23	teu	23	c	no	Mc
teu33	teu	33	h	no	Mh
teu52	teu	52	l	yes	Ml
thɐŋ332	thɐŋ	332	k	no	Nk
thɐŋ452	thɐŋ	452	p	no	Np

Table S6. The 52 pairs of stimuli used in the ‘different’ task, showing the segments and tone, orded alphabetically; some are actual words in the language (also maked using bold). Please note that the reverse order of the stimuli is not shown.

Syllabe 1	Syllabe 2	Segments 1	Tone 1	Tone letter 1	Is real word 1?	Segments 2	Tone 2	Tone letter 2	Is real word 2?	Short name
khem332	khem335	khem	332	k	no	khem	335	t	no	Akt
khem335	khem332	khem	335	t	no	khem	332	k	no	Atk
koi33	koi212	koi	33	h	no	koi	212	x	no	Bhx
koi35	koi32	koi	35	s	no	koi	32	v	no	Bsv
koi32	koi35	koi	32	v	no	koi	35	s	no	Bvs
koi212	koi33	koi	212	x	no	koi	33	h	no	Bxh
pem33	pem32	pem	33	h	no	pem	32	v	yes	Chv
pem32	pem33	pem	32	v	yes	pem	33	h	no	Cvh
pju23	pju33	pju	23	c	no	pju	33	h	no	Dch
pju23	pju212	pju	23	c	no	pju	212	x	no	Dcx
pju33	pju23	pju	33	h	no	pju	23	c	no	Dhc
pju212	pju23	pju	212	x	no	pju	23	c	no	Dxc
poi23	poi52	poi	23	c	no	poi	52	l	no	Ecl
poi23	poi212	poi	23	c	no	poi	212	x	no	Ecx
poi52	poi23	poi	52	l	no	poi	23	c	no	Elc
poi52	poi212	poi	52	l	no	poi	212	x	no	Elx
poi212	poi23	poi	212	x	no	poi	23	c	no	Exc
poi212	poi52	poi	212	x	no	poi	52	l	no	Exl
pui35	pui32	pui	35	s	no	pui	32	v	no	Fsv
pui32	pui35	pui	32	v	no	pui	35	s	no	Fvs
sai335	sai212	sai	335	t	no	sai	212	x	no	Gtx
sai212	sai335	sai	212	x	no	sai	335	t	no	Gxt
sɐm35	sɐm335	sɐm	35	s	yes	sɐm	335	t	no	Hst
sɐm335	sɐm35	sɐm	335	t	no	sɐm	35	s	yes	Hts
sem23	sem452	sem	23	c	no	sem	452	p	no	Icp
sem332	sem35	sem	332	k	no	sem	35	s	no	Iks
sem452	sem23	sem	452	p	no	sem	23	c	no	Ipc
sem452	sem32	sem	452	p	no	sem	32	v	no	Ipv
sem452	sem212	sem	452	p	no	sem	212	x	no	Ipx
sem35	sem332	sem	35	s	no	sem	332	k	no	Isk
sem35	sem335	sem	35	s	no	sem	335	t	no	Ist
sem335	sem35	sem	335	t	no	sem	35	s	no	Its
sem32	sem452	sem	32	v	no	sem	452	p	no	Ivp
sem212	sem452	sem	212	x	no	sem	452	p	no	Ixp
seu332	seu212	seu	332	k	no	seu	212	x	no	Jkx
seu212	seu332	seu	212	x	no	seu	332	k	no	Jxk
səu332	səu52	səu	332	k	no	səu	52	l	no	Kkl
səu52	səu332	səu	52	l	no	səu	332	k	no	Klk
som332	som52	som	332	k	no	som	52	l	no	Lkl
som52	som332	som	52	l	no	som	332	k	no	Llk
som52	som452	som	52	l	no	som	452	p	no	Llp
som452	som52	som	452	p	no	som	52	l	no	Lpl
som452	som32	som	452	p	no	som	32	v	no	Lpv
som32	som452	som	32	v	no	som	452	p	no	Lvp
teu23	teu33	teu	23	c	no	teu	33	h	no	Mch
teu23	teu52	teu	23	c	no	teu	52	l	yes	Mcl
teu33	teu23	teu	33	h	no	teu	23	c	no	Mhc
teu33	teu52	teu	33	h	no	teu	52	l	yes	Mhl
teu52	teu23	teu	52	l	yes	teu	23	c	no	Mlc
teu52	teu33	teu	52	l	yes	teu	33	h	no	Mlh
thɐŋ332	thɐŋ452	thɐŋ	332	k	no	thɐŋ	452	p	no	Nkp
thɐŋ452	thɐŋ332	thɐŋ	452	p	no	thɐŋ	332	k	no	Npk

Please note that stimuli sem³⁵:sem³³⁵ (Ist), sɐm³⁵:sɐm³³⁵ (Hst) are considered as “difficult” by the task designers in the sense that it is hard to hear the difference in tones even for these highly trained speakers of a tone language (Manxiang Wu, p.c.).

2.4.1 Descriptives

We begin this analysis based on the “6 steps” approach of Dima (2018) and the accompanying R code available at https://github.com/alexadima/6-steps-protocol.

2.4.1.1 Percent correct responses

Please note that we will a short notation for the items composed of the segment CAPITAL letter, followed by the one (for ‘same’) or two (for ‘different’) letter tone notation, and the presentation number (e.g., Its2 is the 2nd presentation of the ‘different’ item sem³³⁵:sem³⁵).

Table S7. Frequencies of ‘yes’ responses (the items are ordered by % correct responses). The star (*) denotes those itmes that contain real words in the language.

Item (short name)	# correct responses	% correct responses
Its2	68	13.8%
Hst2*	74	15.0%
Hst1*	77	15.7%
Hts2*	80	16.3%
Its1	88	17.9%
Ist2	89	18.1%
Ist1	90	18.3%
Hts1*	97	19.7%
Bxh2	99	20.1%
Bxh1	104	21.1%
Bhx2	110	22.4%
Llp1	113	23.0%
Bhx1	116	23.6%
Llp2	122	24.8%
Lpl2	123	25.0%
Klk1	128	26.0%
Lpl1	134	27.2%
Klk2	135	27.4%
Kkl2	181	36.8%
Kkl1	199	40.4%
Dhc1	233	47.4%
Dhc2	239	48.6%
Dch2	266	54.1%
Mhc2	281	57.1%
Dch1	285	57.9%
Mhc1	288	58.5%
Llk2	292	59.3%
Llk1	301	61.2%
Mch2	303	61.6%
Npk2	303	61.6%
Mch1	306	62.2%
Lkl2	313	63.6%
Ipv1	326	66.3%
Ipv2	333	67.7%
Npk1	336	68.3%
Lpv1	337	68.5%
Nkp2	341	69.3%
Lpv2	343	69.7%
Ivp2	352	71.5%
Lvp2	353	71.7%
Lkl1	357	72.6%
Mc1	357	72.6%
Nkp1	357	72.6%
Ivp1	360	73.2%
Mc2	360	73.2%
Lvp1	362	73.6%
Exc2	363	73.8%
Dxc2	366	74.4%
Mc3	366	74.4%
Ecx2	371	75.4%
Dxc1	372	75.6%
Ecx1	373	75.8%
Exc1	379	77.0%
Chv1*	384	78.0%
Atk1	386	78.5%
Mc4	386	78.5%
Atk2	390	79.3%
Dcx2	390	79.3%
Chv2*	400	81.3%
Bsv2	404	82.1%
Akt1	405	82.3%
Dcx1	406	82.5%
Bx1	407	82.7%
Isk2	410	83.3%
Akt2	413	83.9%
Ex1	413	83.9%
Iks2	413	83.9%
Jx1	414	84.1%
Elc2	415	84.3%
Icp2	415	84.3%
Iks1	415	84.3%
Ixp2	416	84.6%
Cvh1*	417	84.8%
Gx2	417	84.8%
Ipc2	417	84.8%
Isk1	418	85.0%
Gx1	419	85.2%
Bv1	421	85.6%
Fsv2	422	85.8%
Bsv1	423	86.0%
Elx1	423	86.0%
Gxt2	423	86.0%
Jxk2	423	86.0%
Cvh2*	424	86.2%
Ecl2	424	86.2%
Elc1	424	86.2%
Exl2	424	86.2%
Ml1*	425	86.4%
Elx2	426	86.6%
Ex3	426	86.6%
Ipx2	426	86.6%
Ll1	426	86.6%
Bx2	427	86.8%
Ecl1	427	86.8%
Fsv1	427	86.8%
Bvs2	428	87.0%
Lk2	428	87.0%
Fvs2	429	87.2%
Ll2	429	87.2%
Dx1	430	87.4%
Fvs1	430	87.4%
Gtx2	430	87.4%
Jk1	430	87.4%
Jxk1	430	87.4%
Mh2	430	87.4%
Mhl2*	430	87.4%
Mlh2*	430	87.4%
Icp1	431	87.6%
Mh1	431	87.6%
Bh1	432	87.8%
Dh1	432	87.8%
Ip1	432	87.8%
Ip2	432	87.8%
Ixp1	432	87.8%
Jkx1	432	87.8%
Jkx2	432	87.8%
Ll3	432	87.8%
Mlc1*	432	87.8%
Bv2	433	88.0%
Ex4	433	88.0%
Fv1	433	88.0%
Jx2	433	88.0%
Mcl2*	433	88.0%
Mhl1*	433	88.0%
Ch1	434	88.2%
Ipc1	434	88.2%
Lk1	434	88.2%
Np1	434	88.2%
Bvs1	435	88.4%
Exl1	435	88.4%
Ic1	435	88.4%
Is1	435	88.4%
Ix1	435	88.4%
Ll4	435	88.4%
Mlc2*	435	88.4%
Ak2	436	88.6%
Lv2	436	88.6%
El3	438	89.0%
Ip6	438	89.0%
Ipx1	438	89.0%
Bs1	439	89.2%
Lp1	439	89.2%
Dx2	440	89.4%
El2	440	89.4%
Ip4	440	89.4%
Lv1	440	89.4%
Mcl1*	440	89.4%
Mh3	440	89.4%
Mlh1*	440	89.4%
Ch2	441	89.6%
Cv1*	441	89.6%
El4	441	89.6%
Ak1	442	89.8%
Gxt1	442	89.8%
Ht2	442	89.8%
Ip5	442	89.8%
It2	442	89.8%
Lp2	442	89.8%
Ml3*	442	89.8%
Ex2	443	90.0%
Ic2	443	90.0%
Ip3	443	90.0%
Iv2	443	90.0%
Kk1	443	90.0%
Nk1	443	90.0%
At1	444	90.2%
Gtx1	444	90.2%
Is2	444	90.2%
Ix2	444	90.2%
Bs2	445	90.4%
Dc3	445	90.4%
El1	445	90.4%
Cv2*	446	90.7%
Fs1	446	90.7%
Ht1	446	90.7%
Ik2	446	90.7%
Dc4	447	90.9%
It1	447	90.9%
Iv1	447	90.9%
Jk2	447	90.9%
Lp3	448	91.1%
Mh4	448	91.1%
Ml2*	448	91.1%
Dc1	449	91.3%
Dh2	449	91.3%
Ik1	449	91.3%
Is4	449	91.3%
Bh2	450	91.5%
Ec1	450	91.5%
Ec2	450	91.5%
Gt1	450	91.5%
Lp4	450	91.5%
Fs2	451	91.7%
Nk2	451	91.7%
Ec4	452	91.9%
Is3	452	91.9%
Ml4*	453	92.1%
Gt2	454	92.3%
Hs1*	454	92.3%
Ec3	455	92.5%
Fv2	455	92.5%
Dc2	456	92.7%
At2	457	92.9%
Kl2	457	92.9%
Np2	459	93.3%
Kk2	461	93.7%
Kl1	461	93.7%
Hs2*	463	94.1%

Figure S17. Endorsement frequencies by item (items ordered by % correct responses). Figure generated using R version 4.3.3 (2024-02-29)

2.4.1.2 Correlations between items

Figure S18. Correlation matrix between items. Figure generated using R version 4.3.3 (2024-02-29)

It can be seen that the tetrachoric correlations between the items are rather low, varying between -0.55 and 0.69, with a mean of 0.24, a median of 0.3, sd of 0.25 and IQR of 0.31:

Figure S19. Histogram of the tetracoric correlations between different itmes. Figure generated using R version 4.3.3 (2024-02-29)

Figure S20. Hierarchical clustering of the items using 1 - tetrachoric correlations. Figure generated using R version 4.3.3 (2024-02-29)

Figure S21. Mean tetrachoric correlation with the other items. Figure generated using R version 4.3.3 (2024-02-29)

2.4.1.3 “Weird” items

It can be seen that the following items seem “weird”:

Table S8. ‘Weird’ items that have an average negative correlation with the other items.

Item (short name)	mean tetra. corr.	# correct responses	% correct responses
Bhx1	-0.17	116	23.6%
Bhx2	-0.16	110	22.4%
Bxh1	-0.24	104	21.1%
Bxh2	-0.14	99	20.1%
Dhc1	-0.01	233	47.4%
Hst1*	-0.20	77	15.7%
Hst2*	-0.17	74	15.0%
Hts1*	-0.18	97	19.7%
Hts2*	-0.17	80	16.3%
Ist1	-0.21	90	18.3%
Ist2	-0.20	89	18.1%
Its1	-0.17	88	17.9%
Its2	-0.25	68	13.8%
Kkl2	-0.03	181	36.8%
Klk1	-0.08	128	26.0%
Klk2	-0.08	135	27.4%
Llp1	-0.13	113	23.0%
Llp2	-0.03	122	24.8%
Lpl1	-0.10	134	27.2%
Lpl2	-0.11	123	25.0%

Interestingly, they seem to form meaningful groups:

• the 4 “different” items involving segment B (“koi”) and tones ^h (33) and ^x (212) in both orders and both presentations; they also have very low % correct responses (around 22%) and also have correlations between them (tetrachoric rho’s between 0.28 and 0.39, with mean = 0.33 and sd = 0.04) → this suggests that tones ^h and ^x are very hard to distinguish when paired with segments B;

• the 4 “different” items involving segment H (“sɐm”) and tones ^s (35) and ^t (335) in both orders and both presentations; they also have very low % correct responses (around 17%) and also have correlations between them (tetrachoric rho’s between 0.29 and 0.44, with mean = 0.38 and sd = 0.05) → this suggests that tones ^s and ^t are very hard to distinguish when paired with segments H;

• the 4 “different” items involving segment I (“sem”) and tones ^s (35) and ^t (335) in both orders and both presentations; they also have very low % correct responses (around 17%) and also have correlations between them (tetrachoric rho’s between 0.21 and 0.36, with mean = 0.29 and sd = 0.05) → this suggests that tones ^s and ^t are very hard to distinguish when paired with segments I;

• the 4 “different” items involving segment K (“səu”) and tones ^k (332) and ^l (52) in both orders and both presentations (please note that technically Kkl1 has a very small positive average correlation); they also have very low % correct responses (around 30%) and also have correlations between them (tetrachoric rho’s between 0.2 and 0.41, with mean = 0.29 and sd = 0.08) → this suggests that tones ^k and ^l are very hard to distinguish when paired with segments K;

• the 4 “different” items involving segment L (“som”) and tones ^l (52) and ^p (452) in both orders and both presentations; they also have very low % correct responses (around 25%) and also have correlations between them (tetrachoric rho’s between 0.23 and 0.41, with mean = 0.3 and sd = 0.08) → this suggests that tones ^l and ^p are very hard to distinguish when paired with segments L;

• Dhc1 (involving stimuli pju³³ and pju²³) basically has an average correlation of 0.0, while Dhc2 and Dch1 have a very small positive correlation, but Dch2 has a large positive correlation, suggesting that this may be a different case;

• there are only 4 real words involved in these “special” items: sɐm35 for Hst1, sɐm35 for Hst2, sɐm35 for Hts1, sɐm35 for Hts2.

Moreover:

• there is only one “same” item (Mc4, i.e, the 4th presentation of stimulus teu²³) that has a low positive average correlation;

• the other “different” stimuli involving B (“koi”) (i.e., Bsv1, Bsv2, Bvs1, Bvs2) have around 86% correct responses, and also have tetrachoric correlations between them between 0.41 and 0.59, with mean = 0.49 and sd = 0.06;

• there are no other “different” stimuli involving H (“sɐm”);

• the other “different” stimuli involving I (“sem”) (i.e., Icp1, Icp2, Iks1, Iks2, Ipc1, Ipc2, Ipv1, Ipv2, Ipx1, Ipx2, Isk1, Isk2, Ivp1, Ivp2, Ixp1, Ixp2) have around 82% correct responses, and also have tetrachoric correlations between them between 0.17 and 0.62, with mean = 0.42 and sd = 0.08;

• there are no other “different” stimuli involving K (“səu”);

• the other “different” stimuli involving L (“som”) (i.e., Lkl1, Lkl2, Llk1, Llk2, Lpv1, Lpv2, Lvp1, Lvp2) have around 68% correct responses, and also have tetrachoric correlations between them between 0.21 and 0.47, with mean = 0.36 and sd = 0.06;

• there are no other “different” stimuli involving tones ^h (33) and ^x (212);

• there are no other “different” stimuli involving tones ^s (335) and ^x (212);

• the other “different” stimuli involving tones ^k (332) and ^l (52) (i.e., Lkl1, Lkl2, Llk1, Llk2) have around 64% correct responses, and also have tetrachoric correlations between them between 0.32 and 0.4, with mean = 0.36 and sd = 0.03;

• there are no other “different” stimuli involving tones ^l (52) and ^p (452).

Taken together, these suggest that the four classes of “different” stimuli listed above (i.e., the permutations of B^hx, H^st, I^st, K^kl and L^lp) tend to be massively misinterpreted by the participants (resulting in “incorrect” responses). However, we are missing here crucial items involving the H and K segments and other tone pairs, and the ^hx, ^sx and ^lp pairs of tones involving other segments, to be able to speculate if this is related to the particular combination of segments and tone pairs, or to the segments and/or the tone pairs themselves. It remains an interesting question of why these “different” items, representing 10 or 19.2%, behave differently from the other “different” and from virtually all the “same” items. Apparently, these “incorrect” responses persisted even when some of the participants were provided with explicit feedback by the experimenters (Manxiang Wu, p.c.) suggesting that these perceptions are “real” and not due to inattention or fatigue. Moreover, please note that I^st and H^st are specifically marked as “difficult” by the task creators, suggesting it is the tone pair ^st that is indeed hard to perceive as being different.

It is important to note that there is no tendency for the weird items to contain real words in the language (the table below shows percentages of items that have the corresponding properties):

		is.weird	FALSE	TRUE
is.same	is.word
FALSE	FALSE		39.1	8.7
	TRUE		6.5	2.2
TRUE	FALSE		40.2	0
	TRUE		3.3	0

    Fisher's Exact Test for Count Data

data:  table(d_all_items[!d_all_items$is.same, c("is.word", "is.weird")])
p-value = 0.6415
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
  0.1249375 10.6175357
sample estimates:
odds ratio 
  1.487323 

2.4.1.4 Does the repetition matter?

While most “same” and all “different” items are presented twice, there are some “same” items that are presented more times: Dc (4), Ec (4), El (4), Ex (4), Ip (6), Is (4), Ll (4), Lp (4), Mc (4), Mh (4), Ml (4), with the maximum number of presentations being 6. So, the question is: “do later presentations differ from the earlier ones?”.

Figure S22. Successive presentations for the ‘same’ items: % correct and tetrachoric correlation between the current and the previous presentation. Figure generated using R version 4.3.3 (2024-02-29)

Figure S23. Successive presentations for the ‘different’ items: % correct and tetrachoric correlation between the current and the previous presentation. Figure generated using R version 4.3.3 (2024-02-29)

We fitted a beta regression model (using glmmTMB) of the % correct responses on the interaction between presentation number (between 1 and a maximum of 6, varying by item) and the item type (“same” or “different”). Manual model simplification shows that the interaction and the presentation number do not matter (χ²(2)=0.4, p=0.839, ΔAIC=-3.6), but item type does (χ²(3)=56.5, p=3.21×10^-12, ΔAIC=50.5), with the “same” items having an overall higher % of correct responses than the “different” items (Δ%correct = 21.0%, p=2.51×10^-15). (There is some overdispersion in the model, 1.32, p=0.016, but probably not sufficient to qualitatively change these results.) The same lack of a significant effect for presentation number is shown separately for the ‘same’ items only (p=0.366) and for the ‘different’ items only (p=0.698).

Likewise, it seems the order of the tones for the ‘different’ items does not seem to matter (one-sample t-test against 0 of the differences between the two orders across the items is t(25)=1.5, p=0.156).

Thus, there seems to be no systematic effects of successive presentations of the same item, or of the order of the two tones for the ‘different’ items, on the percent of correct answers.

2.4.2 Item properties: Mokken Scaling Analysis (MSA)

Figure S24. MSA: histogram of the number of Guttman errors (gPlus) across all items. Figure generated using R version 4.3.3 (2024-02-29)

There were 73 cases (out of 492, i.e. 14.8%) with a number of Guttman errors bigger than (Q3 + 1.5*IQR) = 3184.75.

Table S9. MSA: Homogeneity values (with standard errors and 95%CIs) for all items, sorted by item name.

Items	Item H	se	95% ci
Ak1	0.204	(0.028)	[0.150, 0.259]
Ak2	0.216	(0.026)	[0.166, 0.267]
Akt1	0.252	(0.027)	[0.200, 0.305]
Akt2	0.257	(0.027)	[0.204, 0.309]
At1	0.253	(0.024)	[0.205, 0.301]
At2	0.272	(0.025)	[0.223, 0.320]
Atk1	0.224	(0.027)	[0.171, 0.277]
Atk2	0.241	(0.026)	[0.190, 0.293]
Bh1	0.241	(0.023)	[0.197, 0.286]
Bh2	0.236	(0.026)	[0.185, 0.286]
Bhx1	-0.270	(0.057)	[-0.382, -0.157]
Bhx2	-0.263	(0.060)	[-0.379, -0.146]
Bs1	0.270	(0.021)	[0.229, 0.311]
Bs2	0.270	(0.022)	[0.227, 0.313]
Bsv1	0.247	(0.026)	[0.195, 0.298]
Bsv2	0.242	(0.028)	[0.188, 0.296]
Bv1	0.215	(0.026)	[0.164, 0.265]
Bv2	0.219	(0.026)	[0.169, 0.269]
Bvs1	0.244	(0.024)	[0.196, 0.292]
Bvs2	0.261	(0.024)	[0.214, 0.309]
Bx1	0.207	(0.027)	[0.154, 0.260]
Bx2	0.274	(0.023)	[0.228, 0.320]
Bxh1	-0.432	(0.062)	[-0.554, -0.310]
Bxh2	-0.251	(0.064)	[-0.377, -0.125]
Ch1	0.216	(0.024)	[0.169, 0.263]
Ch2	0.285	(0.021)	[0.244, 0.326]
Chv1	0.178	(0.028)	[0.124, 0.232]
Chv2	0.183	(0.027)	[0.129, 0.236]
Cv1	0.201	(0.025)	[0.153, 0.250]
Cv2	0.224	(0.027)	[0.171, 0.277]
Cvh1	0.235	(0.027)	[0.182, 0.287]
Cvh2	0.232	(0.026)	[0.181, 0.283]
Dc1	0.226	(0.024)	[0.179, 0.272]
Dc2	0.241	(0.026)	[0.190, 0.292]
Dc3	0.274	(0.023)	[0.228, 0.320]
Dc4	0.235	(0.024)	[0.188, 0.282]
Dch1	0.106	(0.032)	[0.043, 0.170]
Dch2	0.212	(0.033)	[0.148, 0.275]
Dcx1	0.202	(0.027)	[0.149, 0.255]
Dcx2	0.197	(0.027)	[0.144, 0.250]
Dh1	0.200	(0.026)	[0.150, 0.251]
Dh2	0.159	(0.029)	[0.102, 0.217]
Dhc1	0.021	(0.038)	[-0.053, 0.095]
Dhc2	0.087	(0.037)	[0.014, 0.159]
Dx1	0.206	(0.025)	[0.157, 0.254]
Dx2	0.220	(0.025)	[0.170, 0.269]
Dxc1	0.238	(0.027)	[0.184, 0.291]
Dxc2	0.219	(0.028)	[0.164, 0.273]
Ec1	0.232	(0.024)	[0.184, 0.280]
Ec2	0.242	(0.026)	[0.192, 0.293]
Ec3	0.225	(0.028)	[0.171, 0.279]
Ec4	0.258	(0.024)	[0.212, 0.304]
Ecl1	0.263	(0.025)	[0.213, 0.312]
Ecl2	0.260	(0.025)	[0.211, 0.309]
Ecx1	0.197	(0.028)	[0.143, 0.252]
Ecx2	0.224	(0.028)	[0.170, 0.278]
El1	0.193	(0.028)	[0.138, 0.247]
El2	0.217	(0.025)	[0.169, 0.266]
El3	0.207	(0.027)	[0.154, 0.260]
El4	0.212	(0.026)	[0.160, 0.263]
Elc1	0.290	(0.025)	[0.242, 0.338]
Elc2	0.260	(0.027)	[0.208, 0.312]
Elx1	0.290	(0.025)	[0.242, 0.339]
Elx2	0.294	(0.025)	[0.246, 0.343]
Ex1	0.227	(0.026)	[0.175, 0.278]
Ex2	0.184	(0.025)	[0.135, 0.232]
Ex3	0.227	(0.025)	[0.177, 0.276]
Ex4	0.210	(0.025)	[0.162, 0.259]
Exc1	0.178	(0.028)	[0.123, 0.233]
Exc2	0.191	(0.028)	[0.137, 0.246]
Exl1	0.282	(0.022)	[0.239, 0.326]
Exl2	0.313	(0.024)	[0.266, 0.360]
Fs1	0.267	(0.022)	[0.223, 0.310]
Fs2	0.298	(0.020)	[0.258, 0.337]
Fsv1	0.257	(0.025)	[0.207, 0.306]
Fsv2	0.272	(0.026)	[0.221, 0.323]
Fv1	0.260	(0.023)	[0.214, 0.305]
Fv2	0.267	(0.027)	[0.214, 0.319]
Fvs1	0.285	(0.023)	[0.239, 0.331]
Fvs2	0.243	(0.025)	[0.194, 0.292]
Gt1	0.236	(0.024)	[0.188, 0.284]
Gt2	0.272	(0.024)	[0.224, 0.319]
Gtx1	0.292	(0.019)	[0.256, 0.329]
Gtx2	0.278	(0.024)	[0.230, 0.326]
Gx1	0.172	(0.027)	[0.120, 0.225]
Gx2	0.220	(0.027)	[0.168, 0.272]
Gxt1	0.265	(0.022)	[0.221, 0.309]
Gxt2	0.273	(0.025)	[0.224, 0.323]
Hs1	0.241	(0.026)	[0.189, 0.292]
Hs2	0.269	(0.028)	[0.215, 0.324]
Hst1	-0.378	(0.076)	[-0.528, -0.228]
Hst2	-0.312	(0.077)	[-0.463, -0.162]
Ht1	0.235	(0.025)	[0.187, 0.284]
Ht2	0.244	(0.025)	[0.194, 0.294]
Hts1	-0.303	(0.067)	[-0.434, -0.171]
Hts2	-0.318	(0.074)	[-0.463, -0.173]
Ic1	0.205	(0.026)	[0.155, 0.255]
Ic2	0.264	(0.024)	[0.217, 0.311]
Icp1	0.191	(0.026)	[0.139, 0.242]
Icp2	0.246	(0.027)	[0.193, 0.298]
Ik1	0.222	(0.025)	[0.172, 0.272]
Ik2	0.232	(0.027)	[0.180, 0.284]
Iks1	0.204	(0.027)	[0.151, 0.257]
Iks2	0.255	(0.027)	[0.203, 0.308]
Ip1	0.143	(0.026)	[0.093, 0.193]
Ip2	0.225	(0.024)	[0.178, 0.273]
Ip3	0.143	(0.028)	[0.089, 0.197]
Ip4	0.226	(0.026)	[0.176, 0.276]
Ip5	0.214	(0.026)	[0.164, 0.264]
Ip6	0.224	(0.025)	[0.175, 0.274]
Ipc1	0.254	(0.024)	[0.208, 0.301]
Ipc2	0.277	(0.026)	[0.227, 0.327]
Ipv1	0.227	(0.029)	[0.170, 0.284]
Ipv2	0.233	(0.029)	[0.176, 0.289]
Ipx1	0.256	(0.023)	[0.211, 0.301]
Ipx2	0.268	(0.025)	[0.219, 0.317]
Is1	0.265	(0.022)	[0.222, 0.308]
Is2	0.253	(0.023)	[0.208, 0.299]
Is3	0.279	(0.023)	[0.235, 0.324]
Is4	0.270	(0.025)	[0.221, 0.319]
Isk1	0.223	(0.026)	[0.171, 0.274]
Isk2	0.239	(0.026)	[0.187, 0.290]
Ist1	-0.385	(0.071)	[-0.523, -0.246]
Ist2	-0.362	(0.070)	[-0.500, -0.224]
It1	0.224	(0.026)	[0.174, 0.275]
It2	0.207	(0.027)	[0.155, 0.259]
Its1	-0.304	(0.070)	[-0.441, -0.167]
Its2	-0.539	(0.084)	[-0.703, -0.374]
Iv1	0.276	(0.022)	[0.232, 0.319]
Iv2	0.224	(0.024)	[0.177, 0.271]
Ivp1	0.216	(0.028)	[0.162, 0.270]
Ivp2	0.213	(0.028)	[0.158, 0.269]
Ix1	0.191	(0.025)	[0.142, 0.239]
Ix2	0.236	(0.024)	[0.188, 0.284]
Ixp1	0.270	(0.023)	[0.224, 0.316]
Ixp2	0.253	(0.027)	[0.201, 0.305]
Jk1	0.193	(0.025)	[0.145, 0.242]
Jk2	0.241	(0.026)	[0.191, 0.292]
Jkx1	0.273	(0.024)	[0.226, 0.320]
Jkx2	0.254	(0.024)	[0.207, 0.302]
Jx1	0.222	(0.027)	[0.169, 0.275]
Jx2	0.202	(0.027)	[0.149, 0.256]
Jxk1	0.240	(0.026)	[0.189, 0.291]
Jxk2	0.236	(0.026)	[0.185, 0.287]
Kk1	0.251	(0.023)	[0.205, 0.296]
Kk2	0.285	(0.023)	[0.240, 0.329]
Kkl1	0.121	(0.040)	[0.042, 0.199]
Kkl2	-0.018	(0.045)	[-0.106, 0.070]
Kl1	0.287	(0.028)	[0.231, 0.343]
Kl2	0.295	(0.023)	[0.250, 0.340]
Klk1	-0.092	(0.057)	[-0.203, 0.020]
Klk2	-0.103	(0.054)	[-0.210, 0.004]
Lk1	0.245	(0.024)	[0.198, 0.292]
Lk2	0.244	(0.026)	[0.194, 0.295]
Lkl1	0.204	(0.028)	[0.149, 0.259]
Lkl2	0.187	(0.030)	[0.128, 0.246]
Ll1	0.252	(0.026)	[0.201, 0.303]
Ll2	0.203	(0.026)	[0.152, 0.254]
Ll3	0.232	(0.025)	[0.183, 0.280]
Ll4	0.185	(0.027)	[0.131, 0.239]
Llk1	0.150	(0.031)	[0.089, 0.211]
Llk2	0.171	(0.032)	[0.110, 0.233]
Llp1	-0.202	(0.061)	[-0.321, -0.083]
Llp2	-0.028	(0.056)	[-0.138, 0.083]
Lp1	0.205	(0.026)	[0.155, 0.255]
Lp2	0.227	(0.026)	[0.176, 0.278]
Lp3	0.244	(0.024)	[0.198, 0.291]
Lp4	0.267	(0.026)	[0.217, 0.317]
Lpl1	-0.129	(0.054)	[-0.236, -0.023]
Lpl2	-0.163	(0.058)	[-0.277, -0.050]
Lpv1	0.189	(0.029)	[0.133, 0.246]
Lpv2	0.200	(0.029)	[0.144, 0.256]
Lv1	0.200	(0.026)	[0.149, 0.252]
Lv2	0.211	(0.025)	[0.162, 0.259]
Lvp1	0.192	(0.028)	[0.138, 0.247]
Lvp2	0.198	(0.028)	[0.143, 0.253]
Mc1	0.095	(0.028)	[0.039, 0.151]
Mc2	0.062	(0.029)	[0.005, 0.119]
Mc3	0.101	(0.028)	[0.046, 0.157]
Mc4	0.002	(0.026)	[-0.049, 0.054]
Mch1	0.112	(0.031)	[0.051, 0.173]
Mch2	0.187	(0.031)	[0.126, 0.247]
Mcl1	0.280	(0.022)	[0.237, 0.323]
Mcl2	0.275	(0.024)	[0.229, 0.321]
Mh1	0.173	(0.026)	[0.122, 0.224]
Mh2	0.211	(0.025)	[0.161, 0.260]
Mh3	0.215	(0.024)	[0.169, 0.261]
Mh4	0.207	(0.026)	[0.157, 0.258]
Mhc1	0.147	(0.032)	[0.084, 0.209]
Mhc2	0.188	(0.032)	[0.126, 0.251]
Mhl1	0.255	(0.023)	[0.210, 0.301]
Mhl2	0.293	(0.024)	[0.247, 0.339]
Ml1	0.238	(0.025)	[0.189, 0.287]
Ml2	0.209	(0.026)	[0.158, 0.261]
Ml3	0.202	(0.026)	[0.152, 0.253]
Ml4	0.163	(0.031)	[0.103, 0.224]
Mlc1	0.294	(0.023)	[0.249, 0.339]
Mlc2	0.270	(0.022)	[0.228, 0.313]
Mlh1	0.256	(0.023)	[0.211, 0.301]
Mlh2	0.300	(0.023)	[0.255, 0.344]
Nk1	0.233	(0.023)	[0.188, 0.279]
Nk2	0.245	(0.025)	[0.196, 0.293]
Nkp1	0.200	(0.028)	[0.145, 0.255]
Nkp2	0.217	(0.028)	[0.161, 0.273]
Np1	0.241	(0.024)	[0.194, 0.288]
Np2	0.229	(0.033)	[0.165, 0.293]
Npk1	0.174	(0.029)	[0.117, 0.231]
Npk2	0.190	(0.030)	[0.131, 0.250]

Table S10. Same table as bove but sorted by homogeneity.

Items	Item H	se	95% ci
Exl2	0.313	(0.024)	[0.266, 0.360]
Mlh2	0.300	(0.023)	[0.255, 0.344]
Fs2	0.298	(0.020)	[0.258, 0.337]
Kl2	0.295	(0.023)	[0.250, 0.340]
Elx2	0.294	(0.025)	[0.246, 0.343]
Mlc1	0.294	(0.023)	[0.249, 0.339]
Mhl2	0.293	(0.024)	[0.247, 0.339]
Gtx1	0.292	(0.019)	[0.256, 0.329]
Elc1	0.290	(0.025)	[0.242, 0.338]
Elx1	0.290	(0.025)	[0.242, 0.339]
Kl1	0.287	(0.028)	[0.231, 0.343]
Ch2	0.285	(0.021)	[0.244, 0.326]
Fvs1	0.285	(0.023)	[0.239, 0.331]
Kk2	0.285	(0.023)	[0.240, 0.329]
Exl1	0.282	(0.022)	[0.239, 0.326]
Mcl1	0.280	(0.022)	[0.237, 0.323]
Is3	0.279	(0.023)	[0.235, 0.324]
Gtx2	0.278	(0.024)	[0.230, 0.326]
Ipc2	0.277	(0.026)	[0.227, 0.327]
Iv1	0.276	(0.022)	[0.232, 0.319]
Mcl2	0.275	(0.024)	[0.229, 0.321]
Bx2	0.274	(0.023)	[0.228, 0.320]
Dc3	0.274	(0.023)	[0.228, 0.320]
Gxt2	0.273	(0.025)	[0.224, 0.323]
Jkx1	0.273	(0.024)	[0.226, 0.320]
At2	0.272	(0.025)	[0.223, 0.320]
Fsv2	0.272	(0.026)	[0.221, 0.323]
Gt2	0.272	(0.024)	[0.224, 0.319]
Bs1	0.270	(0.021)	[0.229, 0.311]
Bs2	0.270	(0.022)	[0.227, 0.313]
Is4	0.270	(0.025)	[0.221, 0.319]
Ixp1	0.270	(0.023)	[0.224, 0.316]
Mlc2	0.270	(0.022)	[0.228, 0.313]
Hs2	0.269	(0.028)	[0.215, 0.324]
Ipx2	0.268	(0.025)	[0.219, 0.317]
Fs1	0.267	(0.022)	[0.223, 0.310]
Fv2	0.267	(0.027)	[0.214, 0.319]
Lp4	0.267	(0.026)	[0.217, 0.317]
Gxt1	0.265	(0.022)	[0.221, 0.309]
Is1	0.265	(0.022)	[0.222, 0.308]
Ic2	0.264	(0.024)	[0.217, 0.311]
Ecl1	0.263	(0.025)	[0.213, 0.312]
Bvs2	0.261	(0.024)	[0.214, 0.309]
Ecl2	0.260	(0.025)	[0.211, 0.309]
Elc2	0.260	(0.027)	[0.208, 0.312]
Fv1	0.260	(0.023)	[0.214, 0.305]
Ec4	0.258	(0.024)	[0.212, 0.304]
Akt2	0.257	(0.027)	[0.204, 0.309]
Fsv1	0.257	(0.025)	[0.207, 0.306]
Ipx1	0.256	(0.023)	[0.211, 0.301]
Mlh1	0.256	(0.023)	[0.211, 0.301]
Iks2	0.255	(0.027)	[0.203, 0.308]
Mhl1	0.255	(0.023)	[0.210, 0.301]
Ipc1	0.254	(0.024)	[0.208, 0.301]
Jkx2	0.254	(0.024)	[0.207, 0.302]
At1	0.253	(0.024)	[0.205, 0.301]
Is2	0.253	(0.023)	[0.208, 0.299]
Ixp2	0.253	(0.027)	[0.201, 0.305]
Akt1	0.252	(0.027)	[0.200, 0.305]
Ll1	0.252	(0.026)	[0.201, 0.303]
Kk1	0.251	(0.023)	[0.205, 0.296]
Bsv1	0.247	(0.026)	[0.195, 0.298]
Icp2	0.246	(0.027)	[0.193, 0.298]
Lk1	0.245	(0.024)	[0.198, 0.292]
Nk2	0.245	(0.025)	[0.196, 0.293]
Bvs1	0.244	(0.024)	[0.196, 0.292]
Ht2	0.244	(0.025)	[0.194, 0.294]
Lk2	0.244	(0.026)	[0.194, 0.295]
Lp3	0.244	(0.024)	[0.198, 0.291]
Fvs2	0.243	(0.025)	[0.194, 0.292]
Bsv2	0.242	(0.028)	[0.188, 0.296]
Ec2	0.242	(0.026)	[0.192, 0.293]
Atk2	0.241	(0.026)	[0.190, 0.293]
Bh1	0.241	(0.023)	[0.197, 0.286]
Dc2	0.241	(0.026)	[0.190, 0.292]
Hs1	0.241	(0.026)	[0.189, 0.292]
Jk2	0.241	(0.026)	[0.191, 0.292]
Np1	0.241	(0.024)	[0.194, 0.288]
Jxk1	0.240	(0.026)	[0.189, 0.291]
Isk2	0.239	(0.026)	[0.187, 0.290]
Dxc1	0.238	(0.027)	[0.184, 0.291]
Ml1	0.238	(0.025)	[0.189, 0.287]
Bh2	0.236	(0.026)	[0.185, 0.286]
Gt1	0.236	(0.024)	[0.188, 0.284]
Ix2	0.236	(0.024)	[0.188, 0.284]
Jxk2	0.236	(0.026)	[0.185, 0.287]
Cvh1	0.235	(0.027)	[0.182, 0.287]
Dc4	0.235	(0.024)	[0.188, 0.282]
Ht1	0.235	(0.025)	[0.187, 0.284]
Ipv2	0.233	(0.029)	[0.176, 0.289]
Nk1	0.233	(0.023)	[0.188, 0.279]
Cvh2	0.232	(0.026)	[0.181, 0.283]
Ec1	0.232	(0.024)	[0.184, 0.280]
Ik2	0.232	(0.027)	[0.180, 0.284]
Ll3	0.232	(0.025)	[0.183, 0.280]
Np2	0.229	(0.033)	[0.165, 0.293]
Ex1	0.227	(0.026)	[0.175, 0.278]
Ex3	0.227	(0.025)	[0.177, 0.276]
Ipv1	0.227	(0.029)	[0.170, 0.284]
Lp2	0.227	(0.026)	[0.176, 0.278]
Dc1	0.226	(0.024)	[0.179, 0.272]
Ip4	0.226	(0.026)	[0.176, 0.276]
Ec3	0.225	(0.028)	[0.171, 0.279]
Ip2	0.225	(0.024)	[0.178, 0.273]
Atk1	0.224	(0.027)	[0.171, 0.277]
Cv2	0.224	(0.027)	[0.171, 0.277]
Ecx2	0.224	(0.028)	[0.170, 0.278]
Ip6	0.224	(0.025)	[0.175, 0.274]
It1	0.224	(0.026)	[0.174, 0.275]
Iv2	0.224	(0.024)	[0.177, 0.271]
Isk1	0.223	(0.026)	[0.171, 0.274]
Ik1	0.222	(0.025)	[0.172, 0.272]
Jx1	0.222	(0.027)	[0.169, 0.275]
Dx2	0.220	(0.025)	[0.170, 0.269]
Gx2	0.220	(0.027)	[0.168, 0.272]
Bv2	0.219	(0.026)	[0.169, 0.269]
Dxc2	0.219	(0.028)	[0.164, 0.273]
El2	0.217	(0.025)	[0.169, 0.266]
Nkp2	0.217	(0.028)	[0.161, 0.273]
Ak2	0.216	(0.026)	[0.166, 0.267]
Ch1	0.216	(0.024)	[0.169, 0.263]
Ivp1	0.216	(0.028)	[0.162, 0.270]
Bv1	0.215	(0.026)	[0.164, 0.265]
Mh3	0.215	(0.024)	[0.169, 0.261]
Ip5	0.214	(0.026)	[0.164, 0.264]
Ivp2	0.213	(0.028)	[0.158, 0.269]
Dch2	0.212	(0.033)	[0.148, 0.275]
El4	0.212	(0.026)	[0.160, 0.263]
Lv2	0.211	(0.025)	[0.162, 0.259]
Mh2	0.211	(0.025)	[0.161, 0.260]
Ex4	0.210	(0.025)	[0.162, 0.259]
Ml2	0.209	(0.026)	[0.158, 0.261]
Bx1	0.207	(0.027)	[0.154, 0.260]
El3	0.207	(0.027)	[0.154, 0.260]
It2	0.207	(0.027)	[0.155, 0.259]
Mh4	0.207	(0.026)	[0.157, 0.258]
Dx1	0.206	(0.025)	[0.157, 0.254]
Ic1	0.205	(0.026)	[0.155, 0.255]
Lp1	0.205	(0.026)	[0.155, 0.255]
Ak1	0.204	(0.028)	[0.150, 0.259]
Iks1	0.204	(0.027)	[0.151, 0.257]
Lkl1	0.204	(0.028)	[0.149, 0.259]
Ll2	0.203	(0.026)	[0.152, 0.254]
Dcx1	0.202	(0.027)	[0.149, 0.255]
Jx2	0.202	(0.027)	[0.149, 0.256]
Ml3	0.202	(0.026)	[0.152, 0.253]
Cv1	0.201	(0.025)	[0.153, 0.250]
Dh1	0.200	(0.026)	[0.150, 0.251]
Lpv2	0.200	(0.029)	[0.144, 0.256]
Lv1	0.200	(0.026)	[0.149, 0.252]
Nkp1	0.200	(0.028)	[0.145, 0.255]
Lvp2	0.198	(0.028)	[0.143, 0.253]
Dcx2	0.197	(0.027)	[0.144, 0.250]
Ecx1	0.197	(0.028)	[0.143, 0.252]
El1	0.193	(0.028)	[0.138, 0.247]
Jk1	0.193	(0.025)	[0.145, 0.242]
Lvp1	0.192	(0.028)	[0.138, 0.247]
Exc2	0.191	(0.028)	[0.137, 0.246]
Icp1	0.191	(0.026)	[0.139, 0.242]
Ix1	0.191	(0.025)	[0.142, 0.239]
Npk2	0.190	(0.030)	[0.131, 0.250]
Lpv1	0.189	(0.029)	[0.133, 0.246]
Mhc2	0.188	(0.032)	[0.126, 0.251]
Lkl2	0.187	(0.030)	[0.128, 0.246]
Mch2	0.187	(0.031)	[0.126, 0.247]
Ll4	0.185	(0.027)	[0.131, 0.239]
Ex2	0.184	(0.025)	[0.135, 0.232]
Chv2	0.183	(0.027)	[0.129, 0.236]
Chv1	0.178	(0.028)	[0.124, 0.232]
Exc1	0.178	(0.028)	[0.123, 0.233]
Npk1	0.174	(0.029)	[0.117, 0.231]
Mh1	0.173	(0.026)	[0.122, 0.224]
Gx1	0.172	(0.027)	[0.120, 0.225]
Llk2	0.171	(0.032)	[0.110, 0.233]
Ml4	0.163	(0.031)	[0.103, 0.224]
Dh2	0.159	(0.029)	[0.102, 0.217]
Llk1	0.150	(0.031)	[0.089, 0.211]
Mhc1	0.147	(0.032)	[0.084, 0.209]
Ip1	0.143	(0.026)	[0.093, 0.193]
Ip3	0.143	(0.028)	[0.089, 0.197]
Kkl1	0.121	(0.040)	[0.042, 0.199]
Mch1	0.112	(0.031)	[0.051, 0.173]
Dch1	0.106	(0.032)	[0.043, 0.170]
Mc3	0.101	(0.028)	[0.046, 0.157]
Mc1	0.095	(0.028)	[0.039, 0.151]
Dhc2	0.087	(0.037)	[0.014, 0.159]
Mc2	0.062	(0.029)	[0.005, 0.119]
Dhc1	0.021	(0.038)	[-0.053, 0.095]
Mc4	0.002	(0.026)	[-0.049, 0.054]
Kkl2	-0.018	(0.045)	[-0.106, 0.070]
Llp2	-0.028	(0.056)	[-0.138, 0.083]
Klk1	-0.092	(0.057)	[-0.203, 0.020]
Klk2	-0.103	(0.054)	[-0.210, 0.004]
Lpl1	-0.129	(0.054)	[-0.236, -0.023]
Lpl2	-0.163	(0.058)	[-0.277, -0.050]
Llp1	-0.202	(0.061)	[-0.321, -0.083]
Bxh2	-0.251	(0.064)	[-0.377, -0.125]
Bhx2	-0.263	(0.060)	[-0.379, -0.146]
Bhx1	-0.270	(0.057)	[-0.382, -0.157]
Hts1	-0.303	(0.067)	[-0.434, -0.171]
Its1	-0.304	(0.070)	[-0.441, -0.167]
Hst2	-0.312	(0.077)	[-0.463, -0.162]
Hts2	-0.318	(0.074)	[-0.463, -0.173]
Ist2	-0.362	(0.070)	[-0.500, -0.224]
Hst1	-0.378	(0.076)	[-0.528, -0.228]
Ist1	-0.385	(0.071)	[-0.523, -0.246]
Bxh1	-0.432	(0.062)	[-0.554, -0.310]
Its2	-0.539	(0.084)	[-0.703, -0.374]

The complete item set has a homogeneity value H (se, 95%CI) of 0.201, (0.008), [0.186, 0.216]: this is significantly lower than the recommended 0.30, suggesting that the scale is not homogeneous. This is further supported by the fact that few items have a homogeneity around or above this value (only 2 if we consider the point estimate, and 59 out of 208 if we consider a 95%CI with an upper limit above 0.3). Interestingly, the homogeneity of related items (different presentations and different orders of the tones) are overall very similar, suggesting again that this an intrinsic property of the segments and tone(s) and not of their repeated presentation or of the order of tones.

Table S11. MSA: aisp for increasing H thresholds (c) for all items.

Items	c=0.05	c=0.10	c=0.15	c=0.20	c=0.25	c=0.30	c=0.35	c=0.40	c=0.45	c=0.50	c=0.55	c=0.60
Ak1	1	1	1	1	1	0	25	0	0	0	0	0
Ak2	1	1	1	1	1	1	7	15	23	0	0	0
Akt1	1	1	1	1	1	2	2	12	21	29	0	0
Akt2	1	1	1	1	1	2	2	43	0	0	0	0
At1	1	1	1	1	1	1	1	0	0	0	0	0
At2	1	1	1	1	1	1	1	7	12	17	0	0
Atk1	1	1	1	1	1	2	2	38	0	0	0	0
Atk2	1	1	1	1	1	2	2	0	0	0	0	0
Bh1	1	1	1	1	1	1	1	8	0	0	0	0
Bh2	1	1	1	1	1	1	11	24	36	0	0	0
Bhx1	4	4	5	7	14	0	0	0	0	0	0	0
Bhx2	4	4	5	7	9	0	0	0	0	0	0	0
Bs1	1	1	1	1	1	1	1	1	1	0	0	0
Bs2	1	1	1	1	1	1	1	1	1	14	14	0
Bsv1	1	1	1	1	1	2	4	18	31	0	0	0
Bsv2	1	1	1	1	1	2	2	14	20	0	0	0
Bv1	1	1	1	1	1	1	5	10	15	23	0	0
Bv2	1	1	1	1	1	1	16	32	0	0	0	0
Bvs1	1	1	1	1	1	2	2	23	35	0	0	0
Bvs2	1	1	1	1	1	2	2	14	20	27	0	0
Bx1	1	1	1	1	1	1	11	24	41	0	0	0
Bx2	1	1	1	1	1	1	1	1	1	1	1	1
Bxh1	4	4	5	7	14	0	0	0	0	0	0	0
Bxh2	4	4	5	7	0	0	0	0	0	0	0	0
Ch1	1	1	1	1	1	1	5	31	0	0	0	0
Ch2	1	1	1	1	1	1	1	1	9	12	12	0
Chv1	1	1	1	1	6	12	0	0	0	0	0	0
Chv2	5	5	4	4	6	0	22	0	0	0	0	0
Cv1	1	1	1	1	1	10	28	44	0	0	0	0
Cv2	1	1	1	1	1	1	15	30	0	0	0	0
Cvh1	1	1	1	1	1	2	21	42	0	0	0	0
Cvh2	1	1	1	1	1	2	22	39	0	0	0	0
Dc1	1	1	1	1	1	10	12	0	0	0	0	0
Dc2	1	1	1	1	1	1	1	8	11	19	0	0
Dc3	1	1	1	1	1	1	1	1	28	0	0	0
Dc4	1	1	1	1	1	1	5	33	0	0	0	0
Dch1	3	3	3	8	6	13	0	0	0	0	0	0
Dch2	3	3	3	3	6	2	13	6	20	27	0	0
Dcx1	1	1	1	1	3	2	13	26	0	0	0	0
Dcx2	3	3	3	3	3	6	13	26	0	0	0	0
Dh1	1	1	1	1	1	11	0	0	39	0	0	0
Dh2	2	2	2	2	2	3	3	3	4	6	6	0
Dhc1	3	3	4	8	0	0	0	0	0	0	0	0
Dhc2	4	4	4	4	5	13	0	0	0	0	0	0
Dx1	1	1	1	1	1	1	19	37	16	24	0	0
Dx2	1	1	1	1	1	1	24	11	28	0	0	0
Dxc1	3	3	3	3	3	2	23	20	6	8	8	0
Dxc2	1	1	1	1	1	2	0	0	0	0	0	0
Ec1	1	1	1	1	1	1	11	27	0	0	0	0
Ec2	1	1	1	1	1	1	1	8	33	0	0	0
Ec3	1	1	1	1	1	1	9	4	27	0	0	0
Ec4	1	1	1	1	1	1	1	1	1	4	4	4
Ecl1	1	1	1	1	1	2	2	29	0	0	0	0
Ecl2	1	1	1	1	1	2	2	23	0	0	0	0
Ecx1	5	5	4	6	10	0	0	35	24	0	0	0
Ecx2	1	1	1	1	1	2	13	0	17	10	10	0
El1	1	1	1	1	12	11	19	37	0	0	0	0
El2	1	1	1	1	1	1	11	27	0	0	0	0
El3	1	1	1	1	1	1	0	33	0	0	0	0
El4	1	1	1	1	1	1	5	33	0	0	0	0
Elc1	1	1	1	1	1	1	2	2	7	0	0	0
Elc2	1	1	1	1	1	2	2	2	37	0	0	0
Elx1	1	1	1	1	1	1	1	2	3	5	0	0
Elx2	1	1	1	1	1	1	2	2	3	5	5	0
Ex1	1	1	1	1	1	1	1	8	13	4	4	4
Ex2	1	1	1	1	0	0	0	0	0	0	0	0
Ex3	1	1	1	1	1	1	1	1	1	2	2	2
Ex4	1	1	1	1	1	1	11	24	36	0	0	0
Exc1	3	3	3	6	10	0	0	0	0	0	0	0
Exc2	3	3	3	3	3	6	4	18	31	0	0	0
Exl1	1	1	1	1	1	2	2	2	3	28	0	0
Exl2	1	1	1	1	1	1	1	2	3	20	0	0
Fs1	1	1	1	1	1	1	1	1	16	24	0	0
Fs2	1	1	1	1	1	1	1	1	10	0	0	0
Fsv1	1	1	1	1	1	2	2	22	34	0	0	0
Fsv2	1	1	1	1	1	2	2	2	37	0	0	0
Fv1	1	1	1	1	1	1	1	7	9	17	0	0
Fv2	1	1	1	1	1	1	2	29	8	11	11	0
Fvs1	1	1	1	1	1	1	2	2	3	0	0	0
Fvs2	1	1	1	1	1	2	2	12	22	30	0	0
Gt1	1	1	1	1	1	1	5	10	39	0	0	0
Gt2	1	1	1	1	1	1	1	1	1	14	14	0
Gtx1	1	1	1	1	1	2	2	2	3	5	5	0
Gtx2	1	1	1	1	1	2	2	2	2	3	3	3
Gx1	1	1	1	1	11	0	6	13	26	31	0	0
Gx2	1	1	1	1	1	1	1	8	11	19	0	0
Gxt1	1	1	1	1	1	2	2	2	2	10	10	0
Gxt2	1	1	1	1	1	2	2	12	21	29	0	0
Hs1	1	1	1	1	1	1	6	13	13	31	0	0
Hs2	1	1	1	1	1	1	1	1	10	13	13	0
Hst1	4	4	5	5	7	0	0	0	0	0	0	0
Hst2	4	4	5	5	7	15	0	0	0	0	0	0
Ht1	1	1	1	1	1	1	12	0	0	0	0	0
Ht2	1	1	1	1	1	1	12	4	5	7	7	0
Hts1	4	4	5	5	7	0	0	0	0	0	0	0
Hts2	4	4	5	5	7	15	0	0	0	0	0	0
Ic1	1	1	1	1	1	1	25	0	33	0	0	0
Ic2	1	1	1	1	1	1	1	1	1	0	0	0
Icp1	3	3	3	3	3	0	14	28	0	0	0	0
Icp2	1	1	1	1	1	2	23	0	0	0	0	0
Ik1	1	1	1	1	1	5	0	0	0	0	0	0
Ik2	1	1	1	1	1	1	7	31	0	0	0	0
Iks1	3	3	3	3	3	8	0	0	0	0	0	0
Iks2	1	1	1	1	1	2	2	2	0	0	0	0
Ip1	1	1	1	0	0	0	0	0	0	0	0	0
Ip2	1	1	1	1	1	1	1	40	0	0	0	0
Ip3	2	2	0	0	0	0	0	0	0	0	0	0
Ip4	1	1	1	1	1	1	6	7	26	0	0	0
Ip5	1	1	1	1	1	1	9	40	0	0	0	0
Ip6	1	1	1	1	1	1	15	30	0	0	0	0
Ipc1	1	1	1	1	1	2	2	12	22	30	0	0
Ipc2	1	1	1	1	1	2	2	2	2	18	0	0
Ipv1	1	1	1	1	1	2	2	5	7	9	9	0
Ipv2	1	1	1	1	1	2	2	2	2	3	3	3
Ipx1	1	1	1	1	1	2	2	5	7	9	9	0
Ipx2	1	1	1	1	1	2	2	20	0	0	0	0
Is1	1	1	1	1	1	1	1	6	9	15	15	0
Is2	1	1	1	1	1	1	10	0	0	0	0	0
Is3	1	1	1	1	1	1	1	4	5	7	7	0
Is4	1	1	1	1	1	1	1	6	9	15	15	0
Isk1	1	1	1	1	1	12	18	36	0	0	0	0
Isk2	1	1	1	1	1	2	23	38	0	0	0	0
Ist1	4	4	0	0	0	0	0	0	0	0	0	0
Ist2	4	4	5	5	9	0	0	0	0	0	0	0
It1	1	1	1	1	1	1	7	15	23	0	0	0
It2	1	1	1	1	1	0	0	0	0	0	0	0
Its1	4	4	5	0	0	0	0	0	0	0	0	0
Its2	4	4	5	5	7	0	0	0	0	0	0	0
Iv1	1	1	1	1	1	1	1	1	30	16	16	0
Iv2	1	1	1	1	1	1	9	21	27	0	0	0
Ivp1	1	1	1	1	1	2	14	28	0	0	0	0
Ivp2	1	1	1	1	1	2	2	23	35	0	0	0
Ix1	1	1	1	1	12	0	11	0	0	0	0	0
Ix2	1	1	1	1	1	9	16	32	0	20	0	0
Ixp1	1	1	1	1	1	2	2	5	17	0	0	0
Ixp2	1	1	1	1	1	2	2	17	29	0	0	0
Jk1	1	1	1	1	11	0	7	15	0	0	0	0
Jk2	1	1	1	1	1	1	28	0	0	0	0	0
Jkx1	1	1	1	1	1	2	2	2	24	0	0	0
Jkx2	1	1	1	1	1	2	2	35	0	0	0	0
Jx1	1	1	1	1	1	1	1	4	5	16	16	0
Jx2	1	1	1	1	1	9	0	0	0	0	0	0
Jxk1	1	1	1	1	1	2	4	9	14	22	0	0
Jxk2	1	1	1	1	1	2	2	38	0	0	0	0
Kk1	1	1	1	1	1	1	1	8	0	0	0	0
Kk2	1	1	1	1	1	1	1	1	1	2	2	2
Kkl1	3	3	3	3	4	7	27	0	0	0	0	0
Kkl2	4	4	5	0	0	7	20	41	0	0	0	0
Kl1	1	1	1	1	1	1	1	1	9	12	12	0
Kl2	1	1	1	1	1	1	1	1	1	1	1	1
Klk1	3	3	3	3	4	4	0	0	0	0	0	0
Klk2	4	4	5	5	13	0	0	0	0	0	0	0
Lk1	1	1	1	1	1	1	12	4	30	0	0	0
Lk2	1	1	1	1	1	1	1	1	10	13	13	0
Lkl1	1	1	1	1	3	7	0	0	25	0	0	0
Lkl2	3	3	3	3	3	7	20	41	0	0	0	0
Ll1	1	1	1	1	1	1	1	11	19	26	0	0
Ll2	1	1	1	1	1	1	9	7	0	0	0	0
Ll3	1	1	1	1	1	1	1	21	27	0	0	0
Ll4	1	1	1	1	8	10	24	0	0	0	0	0
Llk1	3	3	3	3	13	0	0	0	0	0	0	0
Llk2	3	3	3	3	3	7	27	43	0	0	0	0
Llp1	4	4	6	9	0	0	0	0	0	0	0	0
Llp2	3	3	3	3	3	8	8	25	40	0	0	0
Lp1	1	1	1	1	1	14	0	0	0	0	0	0
Lp2	1	1	1	1	1	1	1	16	0	0	0	0
Lp3	1	1	1	1	1	1	1	16	12	25	0	0
Lp4	1	1	1	1	1	1	1	1	8	12	0	0
Lpl1	4	4	6	0	0	0	0	0	0	0	0	0
Lpl2	4	4	6	9	0	0	0	0	0	0	0	0
Lpv1	3	3	3	3	3	8	8	0	0	0	0	0
Lpv2	3	3	3	3	3	8	8	17	29	0	0	0
Lv1	1	1	1	1	1	1	5	10	15	23	0	0
Lv2	1	1	1	1	1	1	5	44	0	0	0	0
Lvp1	3	3	3	3	3	8	8	0	0	0	0	0
Lvp2	3	3	3	3	3	2	8	25	40	0	0	0
Mc1	2	2	2	2	2	3	26	0	0	0	0	0
Mc2	2	2	2	2	2	3	3	3	4	6	6	0
Mc3	2	2	2	2	2	5	0	0	0	0	0	0
Mc4	0	0	2	2	0	0	0	0	0	0	0	0
Mch1	3	3	4	4	5	0	0	0	0	0	0	0
Mch2	5	5	4	4	5	6	4	9	14	22	0	0
Mcl1	1	1	1	1	1	2	2	2	2	21	0	0
Mcl2	1	1	1	1	1	2	2	2	2	18	0	0
Mh1	1	1	1	1	2	5	17	34	0	0	0	0
Mh2	1	1	1	1	1	1	7	45	0	0	0	0
Mh3	1	1	1	1	1	5	17	34	0	0	0	0
Mh4	1	1	1	1	1	5	17	0	0	0	0	0
Mhc1	3	3	4	4	5	0	0	0	38	0	0	0
Mhc2	3	3	3	3	5	6	0	22	34	28	0	0
Mhl1	1	1	1	1	1	2	2	14	25	0	0	0
Mhl2	1	1	1	1	1	2	2	2	2	18	0	0
Ml1	1	1	1	1	1	1	1	1	12	25	0	0
Ml2	1	1	1	1	1	1	7	45	41	0	0	0
Ml3	1	1	1	1	1	3	26	0	0	0	0	0
Ml4	1	1	1	1	8	14	0	0	0	0	0	0
Mlc1	1	1	1	1	1	1	1	2	8	11	11	0
Mlc2	1	1	1	1	1	2	2	2	6	8	8	0
Mlh1	1	1	1	1	1	2	2	20	38	0	0	0
Mlh2	1	1	1	1	1	1	2	2	18	21	0	0
Nk1	1	1	1	1	1	1	6	39	0	0	0	0
Nk2	1	1	1	1	1	1	18	36	0	0	0	0
Nkp1	1	1	1	1	4	0	0	0	18	0	0	0
Nkp2	5	5	4	6	4	2	21	42	0	0	0	0
Np1	1	1	1	1	1	1	1	1	16	2	0	0
Np2	1	1	1	1	1	1	9	11	19	26	0	0
Npk1	3	3	3	3	4	4	10	19	32	0	0	0
Npk2	3	3	3	3	4	4	10	19	32	0	0	0

These results suggest that there probably are more than 1 scales, and that some items behave very similarly, but before deciding if we continue we continue in this direction, let’s see what PCA/FA might suggest.

2.4.3 PCA and FA

For PCA, PC₁ explains 20.1% of the variance, followed by PC₂ which explains 5.5%, suggesting there is a main factor on which most items load, but the story is bit more complex, with at least a 2nd factor needed (and a lot of variation remaining unexplained by these 2 components):

Figure S25. Screeplot of the PCA of all the tone items together. Figure generated using R version 4.3.3 (2024-02-29)

Figure S26. Loading of the tone items on the first 2 PCs. Figure generated using R version 4.3.3 (2024-02-29)

Figure S27. The participants plotted on the first 2 PCs, colored by their their qualities of representation (cos2). Figure generated using R version 4.3.3 (2024-02-29)

The actual loadings on the first two PCs are:

	PC1	PC2
Ak1	-0.07	-0.05
Ak2	-0.08	-0.06
Akt1	-0.07	0.09
Akt2	-0.07	0.09
At1	-0.08	-0.03
At2	-0.08	-0.04
Atk1	-0.06	0.10
Atk2	-0.06	0.09
Bh1	-0.08	-0.03
Bh2	-0.07	-0.04
Bhx1	0.05	0.06
Bhx2	0.04	0.06
Bs1	-0.09	-0.04
Bs2	-0.09	-0.05
Bsv1	-0.07	0.08
Bsv2	-0.07	0.10
Bv1	-0.08	-0.05
Bv2	-0.08	-0.04
Bvs1	-0.07	0.08
Bvs2	-0.08	0.08
Bx1	-0.07	-0.03
Bx2	-0.10	-0.04
Bxh1	0.06	0.01
Bxh2	0.04	0.04
Ch1	-0.08	-0.07
Ch2	-0.10	-0.05
Chv1	-0.04	0.09
Chv2	-0.05	0.09
Cv1	-0.07	-0.06
Cv2	-0.07	-0.04
Cvh1	-0.07	0.09
Cvh2	-0.07	0.07
Dc1	-0.07	-0.02
Dc2	-0.07	-0.07
Dc3	-0.09	-0.07
Dc4	-0.08	-0.05
Dch1	-0.01	0.10
Dch2	-0.03	0.09
Dcx1	-0.05	0.10
Dcx2	-0.05	0.10
Dh1	-0.07	-0.04
Dh2	-0.06	-0.07
Dhc1	0.01	0.08
Dhc2	0.00	0.10
Dx1	-0.07	-0.07
Dx2	-0.08	-0.06
Dxc1	-0.06	0.09
Dxc2	-0.05	0.10
Ec1	-0.08	-0.06
Ec2	-0.08	-0.05
Ec3	-0.07	-0.06
Ec4	-0.08	-0.08
Ecl1	-0.08	0.08
Ecl2	-0.08	0.09
Ecx1	-0.05	0.08
Ecx2	-0.05	0.10
El1	-0.07	-0.06
El2	-0.08	-0.07
El3	-0.07	-0.06
El4	-0.08	-0.08
Elc1	-0.09	0.08
Elc2	-0.07	0.10
Elx1	-0.09	0.05
Elx2	-0.09	0.07
Ex1	-0.08	-0.02
Ex2	-0.06	-0.04
Ex3	-0.09	-0.09
Ex4	-0.08	-0.06
Exc1	-0.04	0.09
Exc2	-0.04	0.13
Exl1	-0.09	0.07
Exl2	-0.10	0.06
Fs1	-0.09	-0.05
Fs2	-0.09	-0.03
Fsv1	-0.08	0.07
Fsv2	-0.08	0.09
Fv1	-0.09	-0.03
Fv2	-0.08	-0.01
Fvs1	-0.09	0.07
Fvs2	-0.07	0.09
Gt1	-0.07	-0.04
Gt2	-0.08	-0.05
Gtx1	-0.09	0.07
Gtx2	-0.08	0.09
Gx1	-0.06	-0.03
Gx2	-0.08	-0.04
Gxt1	-0.08	0.08
Gxt2	-0.08	0.09
Hs1	-0.07	-0.04
Hs2	-0.07	-0.06
Hst1	0.05	0.06
Hst2	0.05	0.07
Ht1	-0.08	-0.05
Ht2	-0.08	-0.04
Hts1	0.05	0.05
Hts2	0.05	0.06
Ic1	-0.07	-0.05
Ic2	-0.09	-0.05
Icp1	-0.06	0.08
Icp2	-0.07	0.07
Ik1	-0.07	-0.04
Ik2	-0.08	-0.05
Iks1	-0.06	0.09
Iks2	-0.07	0.09
Ip1	-0.05	-0.02
Ip2	-0.08	-0.05
Ip3	-0.05	-0.05
Ip4	-0.08	-0.05
Ip5	-0.08	-0.06
Ip6	-0.08	-0.03
Ipc1	-0.08	0.08
Ipc2	-0.08	0.08
Ipv1	-0.05	0.08
Ipv2	-0.05	0.10
Ipx1	-0.08	0.07
Ipx2	-0.08	0.07
Is1	-0.09	-0.02
Is2	-0.08	-0.01
Is3	-0.09	-0.05
Is4	-0.09	-0.04
Isk1	-0.07	0.05
Isk2	-0.07	0.09
Ist1	0.05	0.05
Ist2	0.05	0.06
It1	-0.08	-0.05
It2	-0.07	-0.03
Its1	0.04	0.05
Its2	0.07	0.07
Iv1	-0.09	-0.05
Iv2	-0.08	-0.06
Ivp1	-0.05	0.10
Ivp2	-0.05	0.09
Ix1	-0.07	-0.06
Ix2	-0.08	-0.02
Ixp1	-0.08	0.07
Ixp2	-0.07	0.09
Jk1	-0.07	-0.02
Jk2	-0.08	-0.04
Jkx1	-0.08	0.08
Jkx2	-0.08	0.08
Jx1	-0.08	-0.05
Jx2	-0.07	-0.02
Jxk1	-0.07	0.07
Jxk2	-0.07	0.09
Kk1	-0.08	-0.04
Kk2	-0.08	-0.08
Kkl1	-0.01	0.08
Kkl2	0.01	0.07
Kl1	-0.08	-0.06
Kl2	-0.09	-0.05
Klk1	0.03	0.09
Klk2	0.03	0.08
Lk1	-0.08	-0.03
Lk2	-0.09	-0.06
Lkl1	-0.04	0.10
Lkl2	-0.03	0.11
Ll1	-0.09	-0.02
Ll2	-0.07	-0.06
Ll3	-0.08	-0.07
Ll4	-0.07	-0.06
Llk1	-0.02	0.09
Llk2	-0.02	0.10
Llp1	0.04	0.08
Llp2	0.02	0.08
Lp1	-0.07	-0.02
Lp2	-0.08	-0.05
Lp3	-0.08	-0.06
Lp4	-0.09	-0.05
Lpl1	0.03	0.06
Lpl2	0.03	0.06
Lpv1	-0.03	0.12
Lpv2	-0.04	0.12
Lv1	-0.07	-0.07
Lv2	-0.08	-0.06
Lvp1	-0.04	0.11
Lvp2	-0.04	0.13
Mc1	-0.03	-0.05
Mc2	-0.03	-0.08
Mc3	-0.04	-0.07
Mc4	-0.01	-0.07
Mch1	-0.02	0.08
Mch2	-0.04	0.08
Mcl1	-0.08	0.07
Mcl2	-0.09	0.08
Mh1	-0.06	-0.06
Mh2	-0.07	-0.05
Mh3	-0.07	-0.05
Mh4	-0.07	-0.05
Mhc1	-0.03	0.07
Mhc2	-0.03	0.09
Mhl1	-0.08	0.08
Mhl2	-0.09	0.10
Ml1	-0.08	-0.06
Ml2	-0.07	-0.07
Ml3	-0.07	-0.06
Ml4	-0.05	-0.05
Mlc1	-0.09	0.04
Mlc2	-0.08	0.10
Mlh1	-0.08	0.06
Mlh2	-0.09	0.07
Nk1	-0.08	-0.04
Nk2	-0.08	-0.03
Nkp1	-0.04	0.09
Nkp2	-0.04	0.10
Np1	-0.09	-0.05
Np2	-0.07	-0.04
Npk1	-0.03	0.10
Npk2	-0.03	0.09

It can be seen that:

• the repetitions of the same item tend to have very similar loadings,
• PC1 which opposes Its, Bxh, Ist, Hst, Llp, Klk (all presentations and variants except for Kkl1 and Kkl2 which are close to 0.0; these are all “weird” items) to all the others (excluding Dhc1, Dhc2, Dch1, Kkl1 and Mc4 which are close to 0.0),
• PC2 opposes the ‘same’ to the ‘different’ stimuli.

For EFA, all the preliminary tests suggest that factor analysis is appropriate, with the possible exception of a determinant very close to 0.0 (Kaiser-Meyer-Olkin = 0.89 > 0.60; Bartlett’s test is significant: χ²(21528)=61167.2, p=0; and det(cor(data))=7.5e-64 > 0). However, when it comes to the best number of factors, the various methods diverge, but the overall story seems to be that 1 or 2 factors might be enough (but there seems to be a lot of variation beyond this as well, just as in the case of the PCA):

Figure S28. Screeplot of the observed, simulated and randomized data with 1 standard deviation error bars (as generated by fa.parallel()). Figure generated using R version 4.3.3 (2024-02-29)

Figure S29. Number of factors as suggested by the VSS criterion (top left), the complexity of the solution (top right), BIC (bottom left) and Root Mean Residual (bottom right), as implemented by nfactors(). Figure generated using R version 4.3.3 (2024-02-29)

It seems that a 2FA model is better than a 1FA while a 3rd factor does not improve the fit much, but the fit to the data is far from perfect:

Figure S30. Loadings of the variables in the 2-factors model. Figure generated using R version 4.3.3 (2024-02-29)

and the actual loadings on the two factors are (showing only those ≥ 0.1 in absolute value):

Loadings:
     Factor1 Factor2
Ak1   0.461         
Ak2   0.516         
Akt1          0.499 
Akt2          0.522 
At1   0.482   0.126 
At2   0.472         
Atk1          0.512 
Atk2          0.477 
Bh1   0.450   0.139 
Bh2   0.447         
Bhx1 -0.376         
Bhx2 -0.373         
Bs1   0.535   0.128 
Bs2   0.570         
Bsv1          0.495 
Bsv2          0.533 
Bv1   0.493         
Bv2   0.482         
Bvs1  0.118   0.483 
Bvs2  0.116   0.513 
Bx1   0.416         
Bx2   0.569   0.130 
Bxh1 -0.309  -0.129 
Bxh2 -0.291         
Ch1   0.557         
Ch2   0.585   0.109 
Chv1          0.406 
Chv2          0.426 
Cv1   0.489         
Cv2   0.459         
Cvh1          0.479 
Cvh2  0.127   0.420 
Dc1   0.392   0.128 
Dc2   0.530         
Dc3   0.638         
Dc4   0.523         
Dch1 -0.216   0.338 
Dch2          0.362 
Dcx1          0.479 
Dcx2          0.467 
Dh1   0.442         
Dh2   0.469         
Dhc1 -0.246   0.230 
Dhc2 -0.252   0.327 
Dx1   0.531         
Dx2   0.536         
Dxc1          0.476 
Dxc2          0.475 
Ec1   0.507         
Ec2   0.506         
Ec3   0.497         
Ec4   0.628         
Ecl1  0.144   0.493 
Ecl2  0.108   0.514 
Ecx1          0.399 
Ecx2          0.464 
El1   0.469         
El2   0.565         
El3   0.506         
El4   0.578         
Elc1  0.173   0.528 
Elc2          0.564 
Elx1  0.275   0.437 
Elx2  0.206   0.511 
Ex1   0.406   0.139 
Ex2   0.390         
Ex3   0.647         
Ex4   0.534         
Exc1          0.420 
Exc2 -0.167   0.513 
Exl1  0.205   0.490 
Exl2  0.264   0.495 
Fs1   0.557         
Fs2   0.516   0.153 
Fsv1  0.164   0.461 
Fsv2  0.114   0.543 
Fv1   0.504   0.144 
Fv2   0.369   0.206 
Fvs1  0.196   0.501 
Fvs2          0.510 
Gt1   0.452         
Gt2   0.543         
Gtx1  0.180   0.509 
Gtx2  0.114   0.566 
Gx1   0.357         
Gx2   0.478         
Gxt1  0.141   0.491 
Gxt2  0.119   0.538 
Hs1   0.447         
Hs2   0.520         
Hst1 -0.414         
Hst2 -0.388         
Ht1   0.507         
Ht2   0.494   0.113 
Hts1 -0.361         
Hts2 -0.386         
Ic1   0.492         
Ic2   0.547         
Icp1          0.437 
Icp2  0.145   0.431 
Ik1   0.429         
Ik2   0.491         
Iks1          0.469 
Iks2          0.536 
Ip1   0.298         
Ip2   0.492         
Ip3   0.385         
Ip4   0.507         
Ip5   0.506         
Ip6   0.435   0.137 
Ipc1  0.117   0.506 
Ipc2  0.155   0.498 
Ipv1          0.382 
Ipv2          0.451 
Ipx1  0.156   0.464 
Ipx2  0.166   0.481 
Is1   0.472   0.186 
Is2   0.415   0.189 
Is3   0.556         
Is4   0.513   0.116 
Isk1  0.155   0.371 
Isk2          0.512 
Ist1 -0.389         
Ist2 -0.397         
It1   0.501         
It2   0.404         
Its1 -0.349         
Its2 -0.490         
Iv1   0.575         
Iv2   0.538         
Ivp1          0.475 
Ivp2          0.435 
Ix1   0.482         
Ix2   0.394   0.175 
Ixp1  0.162   0.496 
Ixp2          0.528 
Jk1   0.374   0.116 
Jk2   0.469   0.107 
Jkx1  0.156   0.520 
Jkx2  0.104   0.518 
Jx1   0.512         
Jx2   0.383   0.144 
Jxk1  0.128   0.452 
Jxk2          0.518 
Kk1   0.493   0.105 
Kk2   0.610         
Kkl1 -0.153   0.264 
Kkl2 -0.250   0.178 
Kl1   0.535         
Kl2   0.557         
Klk1 -0.349   0.197 
Klk2 -0.321   0.159 
Lk1   0.480   0.127 
Lk2   0.570         
Lkl1          0.437 
Lkl2 -0.144   0.429 
Ll1   0.451   0.173 
Ll2   0.521         
Ll3   0.571         
Ll4   0.489         
Llk1 -0.133   0.345 
Llk2 -0.141   0.371 
Llp1 -0.386   0.145 
Llp2 -0.301   0.228 
Lp1   0.387   0.127 
Lp2   0.510         
Lp3   0.544         
Lp4   0.528         
Lpl1 -0.285   0.105 
Lpl2 -0.311   0.101 
Lpv1 -0.169   0.467 
Lpv2 -0.154   0.489 
Lv1   0.537         
Lv2   0.529         
Lvp1 -0.114   0.458 
Lvp2 -0.180   0.515 
Mc1   0.276         
Mc2   0.341  -0.162 
Mc3   0.361  -0.128 
Mc4   0.218  -0.173 
Mch1 -0.141   0.303 
Mch2          0.342 
Mcl1  0.183   0.489 
Mcl2  0.151   0.536 
Mh1   0.459         
Mh2   0.483         
Mh3   0.483         
Mh4   0.463         
Mhc1          0.290 
Mhc2 -0.101   0.366 
Mhl1  0.122   0.495 
Mhl2  0.131   0.595 
Ml1   0.549         
Ml2   0.518         
Ml3   0.501         
Ml4   0.396         
Mlc1  0.313   0.422 
Mlc2          0.576 
Mlh1  0.184   0.424 
Mlh2  0.230   0.504 
Nk1   0.471   0.103 
Nk2   0.425   0.128 
Nkp1          0.408 
Nkp2          0.452 
Np1   0.543         
Np2   0.407         
Npk1 -0.118   0.411 
Npk2          0.364 

               Factor1 Factor2
SS loadings     28.929  19.434
Proportion Var   0.139   0.093
Cumulative Var   0.139   0.233

It can be seen that:

• the repetitions of the same item tend to have very similar loadings,
• FA1 is composed of all the ‘same’ items (all presentations, and all with + loadings; please note that the actual sign is arbitrary but the differences in signs matter) and a few ‘different’ items: Bhx, Hst, Ist, Kkl and Llp (all presentations and variants, all with - loadings) – it is interesting to note that all these items are “weird”,
• FA2 is composed of most of the ‘different’ items: Akt, Bsv, Chv, Dch, Dcx, Ecl, Ecx, Elx, Fsv, Gtx, Icp, Iks, Ipv, Ipx, Isk, Jkx, Lkl, Lpv, Mch, Mcl, Mhc, Mhl, and Nkp1 (all presentations and variants, all with + loadings).

Thus, PC1 basically opposes the “weird” items to all the other items, while PC2 opposes the ‘same’ to the ‘different’ items. Likewise, it seems that FA1 really captures the ‘same’ items and the “weird” ‘different’ items (but with opposite signs), while FA2 captures the “normal” ‘different’ items.

These observations prompt the question: are the “weird” items really of the ‘same’ type?

2.4.4 Are the “weird” items really of the ‘same’ type?

All these together suggest the hypothesis that the “weird” stimuli, while designed as ‘different’, are, in fact, perceived as (albeit rather difficult) ‘same’ items by the participants. If true, this hypothesis implies that coding them as such (i.e., in fact flipping their “correct” and “incorrect” responses) should align them with the other ‘same’ items.

2.4.4.1 Percent correct responses

Table S12. ‘Flipped’ weird items: Frequencies of ‘yes’ responses (the items are ordered by % correct responses).

Item (short name)	# correct responses	% correct responses
Dhc1	233	47.4%
Dhc2	239	48.6%
Dch2	266	54.1%
Mhc2	281	57.1%
Dch1	285	57.9%
Mhc1	288	58.5%
Llk2	292	59.3%
Kkl1	293	59.6%
Llk1	301	61.2%
Mch2	303	61.6%
Npk2	303	61.6%
Mch1	306	62.2%
Kkl2	311	63.2%
Lkl2	313	63.6%
Ipv1	326	66.3%
Ipv2	333	67.7%
Npk1	336	68.3%
Lpv1	337	68.5%
Nkp2	341	69.3%
Lpv2	343	69.7%
Ivp2	352	71.5%
Lvp2	353	71.7%
Klk2	357	72.6%
Lkl1	357	72.6%
Mc1	357	72.6%
Nkp1	357	72.6%
Lpl1	358	72.8%
Ivp1	360	73.2%
Mc2	360	73.2%
Lvp1	362	73.6%
Exc2	363	73.8%
Klk1	364	74.0%
Dxc2	366	74.4%
Mc3	366	74.4%
Lpl2	369	75.0%
Llp2	370	75.2%
Ecx2	371	75.4%
Dxc1	372	75.6%
Ecx1	373	75.8%
Bhx1	376	76.4%
Exc1	379	77.0%
Llp1	379	77.0%
Bhx2	382	77.6%
Chv1	384	78.0%
Atk1	386	78.5%
Mc4	386	78.5%
Bxh1	388	78.9%
Atk2	390	79.3%
Dcx2	390	79.3%
Bxh2	393	79.9%
Hts1	395	80.3%
Chv2	400	81.3%
Ist1	402	81.7%
Ist2	403	81.9%
Bsv2	404	82.1%
Its1	404	82.1%
Akt1	405	82.3%
Dcx1	406	82.5%
Bx1	407	82.7%
Isk2	410	83.3%
Hts2	412	83.7%
Akt2	413	83.9%
Ex1	413	83.9%
Iks2	413	83.9%
Jx1	414	84.1%
Elc2	415	84.3%
Hst1	415	84.3%
Icp2	415	84.3%
Iks1	415	84.3%
Ixp2	416	84.6%
Cvh1	417	84.8%
Gx2	417	84.8%
Ipc2	417	84.8%
Hst2	418	85.0%
Isk1	418	85.0%
Gx1	419	85.2%
Bv1	421	85.6%
Fsv2	422	85.8%
Bsv1	423	86.0%
Elx1	423	86.0%
Gxt2	423	86.0%
Jxk2	423	86.0%
Cvh2	424	86.2%
Ecl2	424	86.2%
Elc1	424	86.2%
Exl2	424	86.2%
Its2	424	86.2%
Ml1	425	86.4%
Elx2	426	86.6%
Ex3	426	86.6%
Ipx2	426	86.6%
Ll1	426	86.6%
Bx2	427	86.8%
Ecl1	427	86.8%
Fsv1	427	86.8%
Bvs2	428	87.0%
Lk2	428	87.0%
Fvs2	429	87.2%
Ll2	429	87.2%
Dx1	430	87.4%
Fvs1	430	87.4%
Gtx2	430	87.4%
Jk1	430	87.4%
Jxk1	430	87.4%
Mh2	430	87.4%
Mhl2	430	87.4%
Mlh2	430	87.4%
Icp1	431	87.6%
Mh1	431	87.6%
Bh1	432	87.8%
Dh1	432	87.8%
Ip1	432	87.8%
Ip2	432	87.8%
Ixp1	432	87.8%
Jkx1	432	87.8%
Jkx2	432	87.8%
Ll3	432	87.8%
Mlc1	432	87.8%
Bv2	433	88.0%
Ex4	433	88.0%
Fv1	433	88.0%
Jx2	433	88.0%
Mcl2	433	88.0%
Mhl1	433	88.0%
Ch1	434	88.2%
Ipc1	434	88.2%
Lk1	434	88.2%
Np1	434	88.2%
Bvs1	435	88.4%
Exl1	435	88.4%
Ic1	435	88.4%
Is1	435	88.4%
Ix1	435	88.4%
Ll4	435	88.4%
Mlc2	435	88.4%
Ak2	436	88.6%
Lv2	436	88.6%
El3	438	89.0%
Ip6	438	89.0%
Ipx1	438	89.0%
Bs1	439	89.2%
Lp1	439	89.2%
Dx2	440	89.4%
El2	440	89.4%
Ip4	440	89.4%
Lv1	440	89.4%
Mcl1	440	89.4%
Mh3	440	89.4%
Mlh1	440	89.4%
Ch2	441	89.6%
Cv1	441	89.6%
El4	441	89.6%
Ak1	442	89.8%
Gxt1	442	89.8%
Ht2	442	89.8%
Ip5	442	89.8%
It2	442	89.8%
Lp2	442	89.8%
Ml3	442	89.8%
Ex2	443	90.0%
Ic2	443	90.0%
Ip3	443	90.0%
Iv2	443	90.0%
Kk1	443	90.0%
Nk1	443	90.0%
At1	444	90.2%
Gtx1	444	90.2%
Is2	444	90.2%
Ix2	444	90.2%
Bs2	445	90.4%
Dc3	445	90.4%
El1	445	90.4%
Cv2	446	90.7%
Fs1	446	90.7%
Ht1	446	90.7%
Ik2	446	90.7%
Dc4	447	90.9%
It1	447	90.9%
Iv1	447	90.9%
Jk2	447	90.9%
Lp3	448	91.1%
Mh4	448	91.1%
Ml2	448	91.1%
Dc1	449	91.3%
Dh2	449	91.3%
Ik1	449	91.3%
Is4	449	91.3%
Bh2	450	91.5%
Ec1	450	91.5%
Ec2	450	91.5%
Gt1	450	91.5%
Lp4	450	91.5%
Fs2	451	91.7%
Nk2	451	91.7%
Ec4	452	91.9%
Is3	452	91.9%
Ml4	453	92.1%
Gt2	454	92.3%
Hs1	454	92.3%
Ec3	455	92.5%
Fv2	455	92.5%
Dc2	456	92.7%
At2	457	92.9%
Kl2	457	92.9%
Np2	459	93.3%
Kk2	461	93.7%
Kl1	461	93.7%
Hs2	463	94.1%

Figure S31. ‘Flipped’ weird items: Endorsement frequencies by item (items ordered by % correct responses). Figure generated using R version 4.3.3 (2024-02-29)

2.4.4.2 Correlations between items

Figure S32. ‘Flipped’ weird items: Correlation matrix between items. Figure generated using R version 4.3.3 (2024-02-29)

It can be seen that the tetrachoric correlations between the items are rather low, varying between -0.34 and 0.69, with a mean of 0.3, a median of 0.32, sd of 0.16 and IQR of 0.23:

Figure S33. Histogram of the tetracoric correlations between different itmes. Figure generated using R version 4.3.3 (2024-02-29)

Figure S34. ‘Flipped’ weird items: Hierarchical clustering of the items using 1 - tetrachoric correlations. Figure generated using R version 4.3.3 (2024-02-29)

Figure S35. ‘Flipped’ weird items: Mean correlation with the other items. Figure generated using R version 4.3.3 (2024-02-29)

2.4.4.3 PCA and FA

For PCA, PC₁ explains 20.1% of the variance, followed by PC₂ which explains 5.5%, suggesting there is a main factor on which most items load, but the story is bit more complex, with at least a 2nd factor needed (and a lot of variation remaining unexplained by these 2 components):

Figure S36. ‘Flipped’ weird items: Screeplot of the PCA of all the tone items together. Figure generated using R version 4.3.3 (2024-02-29)

Figure S37. ‘Flipped’ weird items: Loading of the tone items on the first 2 PCs. Figure generated using R version 4.3.3 (2024-02-29)

Figure S38. ‘Flipped’ weird items: The participants plotted on the first 2 PCs, colored by their their qualities of representation (cos2). Figure generated using R version 4.3.3 (2024-02-29)

The actual loadings on the first two PCs are:

	PC1	PC2
Ak1	-0.07	-0.05
Ak2	-0.08	-0.06
Akt1	-0.07	0.09
Akt2	-0.07	0.09
At1	-0.08	-0.03
At2	-0.08	-0.04
Atk1	-0.06	0.10
Atk2	-0.06	0.09
Bh1	-0.08	-0.03
Bh2	-0.07	-0.04
Bhx1	-0.05	-0.06
Bhx2	-0.04	-0.06
Bs1	-0.09	-0.04
Bs2	-0.09	-0.05
Bsv1	-0.07	0.08
Bsv2	-0.07	0.10
Bv1	-0.08	-0.05
Bv2	-0.08	-0.04
Bvs1	-0.07	0.08
Bvs2	-0.08	0.08
Bx1	-0.07	-0.03
Bx2	-0.10	-0.04
Bxh1	-0.06	-0.01
Bxh2	-0.04	-0.04
Ch1	-0.08	-0.07
Ch2	-0.10	-0.05
Chv1	-0.04	0.09
Chv2	-0.05	0.09
Cv1	-0.07	-0.06
Cv2	-0.07	-0.04
Cvh1	-0.07	0.09
Cvh2	-0.07	0.07
Dc1	-0.07	-0.02
Dc2	-0.07	-0.07
Dc3	-0.09	-0.07
Dc4	-0.08	-0.05
Dch1	-0.01	0.10
Dch2	-0.03	0.09
Dcx1	-0.05	0.10
Dcx2	-0.05	0.10
Dh1	-0.07	-0.04
Dh2	-0.06	-0.07
Dhc1	0.01	0.08
Dhc2	0.00	0.10
Dx1	-0.07	-0.07
Dx2	-0.08	-0.06
Dxc1	-0.06	0.09
Dxc2	-0.05	0.10
Ec1	-0.08	-0.06
Ec2	-0.08	-0.05
Ec3	-0.07	-0.06
Ec4	-0.08	-0.08
Ecl1	-0.08	0.08
Ecl2	-0.08	0.09
Ecx1	-0.05	0.08
Ecx2	-0.05	0.10
El1	-0.07	-0.06
El2	-0.08	-0.07
El3	-0.07	-0.06
El4	-0.08	-0.08
Elc1	-0.09	0.08
Elc2	-0.07	0.10
Elx1	-0.09	0.05
Elx2	-0.09	0.07
Ex1	-0.08	-0.02
Ex2	-0.06	-0.04
Ex3	-0.09	-0.09
Ex4	-0.08	-0.06
Exc1	-0.04	0.09
Exc2	-0.04	0.13
Exl1	-0.09	0.07
Exl2	-0.10	0.06
Fs1	-0.09	-0.05
Fs2	-0.09	-0.03
Fsv1	-0.08	0.07
Fsv2	-0.08	0.09
Fv1	-0.09	-0.03
Fv2	-0.08	-0.01
Fvs1	-0.09	0.07
Fvs2	-0.07	0.09
Gt1	-0.07	-0.04
Gt2	-0.08	-0.05
Gtx1	-0.09	0.07
Gtx2	-0.08	0.09
Gx1	-0.06	-0.03
Gx2	-0.08	-0.04
Gxt1	-0.08	0.08
Gxt2	-0.08	0.09
Hs1	-0.07	-0.04
Hs2	-0.07	-0.06
Hst1	-0.05	-0.06
Hst2	-0.05	-0.07
Ht1	-0.08	-0.05
Ht2	-0.08	-0.04
Hts1	-0.05	-0.05
Hts2	-0.05	-0.06
Ic1	-0.07	-0.05
Ic2	-0.09	-0.05
Icp1	-0.06	0.08
Icp2	-0.07	0.07
Ik1	-0.07	-0.04
Ik2	-0.08	-0.05
Iks1	-0.06	0.09
Iks2	-0.07	0.09
Ip1	-0.05	-0.02
Ip2	-0.08	-0.05
Ip3	-0.05	-0.05
Ip4	-0.08	-0.05
Ip5	-0.08	-0.06
Ip6	-0.08	-0.03
Ipc1	-0.08	0.08
Ipc2	-0.08	0.08
Ipv1	-0.05	0.08
Ipv2	-0.05	0.10
Ipx1	-0.08	0.07
Ipx2	-0.08	0.07
Is1	-0.09	-0.02
Is2	-0.08	-0.01
Is3	-0.09	-0.05
Is4	-0.09	-0.04
Isk1	-0.07	0.05
Isk2	-0.07	0.09
Ist1	-0.05	-0.05
Ist2	-0.05	-0.06
It1	-0.08	-0.05
It2	-0.07	-0.03
Its1	-0.04	-0.05
Its2	-0.07	-0.07
Iv1	-0.09	-0.05
Iv2	-0.08	-0.06
Ivp1	-0.05	0.10
Ivp2	-0.05	0.09
Ix1	-0.07	-0.06
Ix2	-0.08	-0.02
Ixp1	-0.08	0.07
Ixp2	-0.07	0.09
Jk1	-0.07	-0.02
Jk2	-0.08	-0.04
Jkx1	-0.08	0.08
Jkx2	-0.08	0.08
Jx1	-0.08	-0.05
Jx2	-0.07	-0.02
Jxk1	-0.07	0.07
Jxk2	-0.07	0.09
Kk1	-0.08	-0.04
Kk2	-0.08	-0.08
Kkl1	0.01	-0.08
Kkl2	-0.01	-0.07
Kl1	-0.08	-0.06
Kl2	-0.09	-0.05
Klk1	-0.03	-0.09
Klk2	-0.03	-0.08
Lk1	-0.08	-0.03
Lk2	-0.09	-0.06
Lkl1	-0.04	0.10
Lkl2	-0.03	0.11
Ll1	-0.09	-0.02
Ll2	-0.07	-0.06
Ll3	-0.08	-0.07
Ll4	-0.07	-0.06
Llk1	-0.02	0.09
Llk2	-0.02	0.10
Llp1	-0.04	-0.08
Llp2	-0.02	-0.08
Lp1	-0.07	-0.02
Lp2	-0.08	-0.05
Lp3	-0.08	-0.06
Lp4	-0.09	-0.05
Lpl1	-0.03	-0.06
Lpl2	-0.03	-0.06
Lpv1	-0.03	0.12
Lpv2	-0.04	0.12
Lv1	-0.07	-0.07
Lv2	-0.08	-0.06
Lvp1	-0.04	0.11
Lvp2	-0.04	0.13
Mc1	-0.03	-0.05
Mc2	-0.03	-0.08
Mc3	-0.04	-0.07
Mc4	-0.01	-0.07
Mch1	-0.02	0.08
Mch2	-0.04	0.08
Mcl1	-0.08	0.07
Mcl2	-0.09	0.08
Mh1	-0.06	-0.06
Mh2	-0.07	-0.05
Mh3	-0.07	-0.05
Mh4	-0.07	-0.05
Mhc1	-0.03	0.07
Mhc2	-0.03	0.09
Mhl1	-0.08	0.08
Mhl2	-0.09	0.10
Ml1	-0.08	-0.06
Ml2	-0.07	-0.07
Ml3	-0.07	-0.06
Ml4	-0.05	-0.05
Mlc1	-0.09	0.04
Mlc2	-0.08	0.10
Mlh1	-0.08	0.06
Mlh2	-0.09	0.07
Nk1	-0.08	-0.04
Nk2	-0.08	-0.03
Nkp1	-0.04	0.09
Nkp2	-0.04	0.10
Np1	-0.09	-0.05
Np2	-0.07	-0.04
Npk1	-0.03	0.10
Npk2	-0.03	0.09

It can be seen that:

• the repetitions of the same item tend to have very similar loadings,
• PC1 is harder to interpret but seems to oppose the vast majority of the items to basically Kkl and Dhc (which have loadings close to 0), while
• PC2 opposes the ‘same’ (including the ‘flipped’ items) to the ‘different’ items.

For EFA, all the preliminary tests suggest that factor analysis is appropriate, with the possible exception of a determinant very close to 0.0 (Kaiser-Meyer-Olkin = 0.89 > 0.60; Bartlett’s test is significant: χ²(21528)=61167.2, p=0; and det(cor(data))=7.5e-64 > 0). However, when it comes to the best number of factors, the various methods diverge, but the overall story seems to be that 1 or 2 factors might be enough (but there seems to be a lot of variation beyond this as well, just as in the case of the PCA):

Figure S39. ‘Flipped’ weird items: Screeplot of the observed, simulated and randomized data with 1 standard deviation error bars (as generated by fa.parallel()). Figure generated using R version 4.3.3 (2024-02-29)

Figure S40. ‘Flipped’ weird items: Number of factors as suggested by the VSS criterion (top left), the complexity of the solution (top right), BIC (bottom left) and Root Mean Residual (bottom right), as implemented by nfactors(). Figure generated using R version 4.3.3 (2024-02-29)

It seems that a 2FA model is better than a 1FA while a 3rd factor does not improve the fit much, but the fit to the data is far from perfect:

Figure S41. ‘Flipped’ weird items: Loadings of the variables in the 2-factors model. Figure generated using R version 4.3.3 (2024-02-29)

and the actual loadings on the two factors are (showing only those ≥ 0.1 in absolute value):

Loadings:
     Factor1 Factor2
Ak1   0.461         
Ak2   0.516         
Akt1          0.499 
Akt2          0.522 
At1   0.482   0.126 
At2   0.472         
Atk1          0.512 
Atk2          0.477 
Bh1   0.450   0.139 
Bh2   0.447         
Bhx1  0.376         
Bhx2  0.373         
Bs1   0.535   0.128 
Bs2   0.570         
Bsv1          0.495 
Bsv2          0.533 
Bv1   0.493         
Bv2   0.482         
Bvs1  0.118   0.483 
Bvs2  0.116   0.513 
Bx1   0.416         
Bx2   0.569   0.130 
Bxh1  0.309   0.129 
Bxh2  0.291         
Ch1   0.557         
Ch2   0.585   0.109 
Chv1          0.406 
Chv2          0.426 
Cv1   0.489         
Cv2   0.459         
Cvh1          0.479 
Cvh2  0.127   0.420 
Dc1   0.392   0.128 
Dc2   0.530         
Dc3   0.638         
Dc4   0.523         
Dch1 -0.216   0.338 
Dch2          0.362 
Dcx1          0.479 
Dcx2          0.467 
Dh1   0.442         
Dh2   0.469         
Dhc1 -0.246   0.230 
Dhc2 -0.252   0.327 
Dx1   0.531         
Dx2   0.536         
Dxc1          0.476 
Dxc2          0.475 
Ec1   0.507         
Ec2   0.506         
Ec3   0.497         
Ec4   0.628         
Ecl1  0.144   0.493 
Ecl2  0.108   0.514 
Ecx1          0.399 
Ecx2          0.464 
El1   0.469         
El2   0.565         
El3   0.506         
El4   0.578         
Elc1  0.173   0.528 
Elc2          0.564 
Elx1  0.275   0.437 
Elx2  0.206   0.511 
Ex1   0.406   0.139 
Ex2   0.390         
Ex3   0.647         
Ex4   0.534         
Exc1          0.420 
Exc2 -0.167   0.513 
Exl1  0.205   0.490 
Exl2  0.264   0.495 
Fs1   0.557         
Fs2   0.516   0.153 
Fsv1  0.164   0.461 
Fsv2  0.114   0.543 
Fv1   0.504   0.144 
Fv2   0.369   0.206 
Fvs1  0.196   0.501 
Fvs2          0.510 
Gt1   0.452         
Gt2   0.543         
Gtx1  0.180   0.509 
Gtx2  0.114   0.566 
Gx1   0.357         
Gx2   0.478         
Gxt1  0.141   0.491 
Gxt2  0.119   0.538 
Hs1   0.447         
Hs2   0.520         
Hst1  0.414         
Hst2  0.388         
Ht1   0.507         
Ht2   0.494   0.113 
Hts1  0.361         
Hts2  0.386         
Ic1   0.492         
Ic2   0.547         
Icp1          0.437 
Icp2  0.145   0.431 
Ik1   0.429         
Ik2   0.491         
Iks1          0.469 
Iks2          0.536 
Ip1   0.298         
Ip2   0.492         
Ip3   0.385         
Ip4   0.507         
Ip5   0.506         
Ip6   0.435   0.137 
Ipc1  0.117   0.506 
Ipc2  0.155   0.498 
Ipv1          0.382 
Ipv2          0.451 
Ipx1  0.156   0.464 
Ipx2  0.166   0.481 
Is1   0.472   0.186 
Is2   0.415   0.189 
Is3   0.556         
Is4   0.513   0.116 
Isk1  0.155   0.371 
Isk2          0.512 
Ist1  0.389         
Ist2  0.397         
It1   0.501         
It2   0.404         
Its1  0.349         
Its2  0.490         
Iv1   0.575         
Iv2   0.538         
Ivp1          0.475 
Ivp2          0.435 
Ix1   0.482         
Ix2   0.394   0.175 
Ixp1  0.162   0.496 
Ixp2          0.528 
Jk1   0.374   0.116 
Jk2   0.469   0.107 
Jkx1  0.156   0.520 
Jkx2  0.104   0.518 
Jx1   0.512         
Jx2   0.383   0.144 
Jxk1  0.128   0.452 
Jxk2          0.518 
Kk1   0.493   0.105 
Kk2   0.610         
Kkl1  0.153  -0.264 
Kkl2  0.250  -0.178 
Kl1   0.535         
Kl2   0.557         
Klk1  0.349  -0.197 
Klk2  0.321  -0.159 
Lk1   0.480   0.127 
Lk2   0.570         
Lkl1          0.437 
Lkl2 -0.144   0.429 
Ll1   0.451   0.173 
Ll2   0.521         
Ll3   0.571         
Ll4   0.489         
Llk1 -0.133   0.345 
Llk2 -0.141   0.371 
Llp1  0.386  -0.145 
Llp2  0.301  -0.228 
Lp1   0.387   0.127 
Lp2   0.510         
Lp3   0.544         
Lp4   0.528         
Lpl1  0.285  -0.105 
Lpl2  0.311  -0.101 
Lpv1 -0.169   0.467 
Lpv2 -0.154   0.489 
Lv1   0.537         
Lv2   0.529         
Lvp1 -0.114   0.458 
Lvp2 -0.180   0.515 
Mc1   0.276         
Mc2   0.341  -0.162 
Mc3   0.361  -0.128 
Mc4   0.218  -0.173 
Mch1 -0.141   0.303 
Mch2          0.342 
Mcl1  0.183   0.489 
Mcl2  0.151   0.536 
Mh1   0.459         
Mh2   0.483         
Mh3   0.483         
Mh4   0.463         
Mhc1          0.290 
Mhc2 -0.101   0.366 
Mhl1  0.122   0.495 
Mhl2  0.131   0.595 
Ml1   0.549         
Ml2   0.518         
Ml3   0.501         
Ml4   0.396         
Mlc1  0.313   0.422 
Mlc2          0.576 
Mlh1  0.184   0.424 
Mlh2  0.230   0.504 
Nk1   0.471   0.103 
Nk2   0.425   0.128 
Nkp1          0.408 
Nkp2          0.452 
Np1   0.543         
Np2   0.407         
Npk1 -0.118   0.411 
Npk2          0.364 

               Factor1 Factor2
SS loadings     28.929  19.434
Proportion Var   0.139   0.093
Cumulative Var   0.139   0.233

It can be seen that:

• the repetitions of the same item tend to have very similar loadings,
• FA1 is composed of all the ‘same’ items, including the “flipped” items Bhx, Hst, Ist, Kkl and Llp (all presentations, and all with loading of the same sign),
• FA2 is composed of all the ‘different’ items (all presentations and variants, all with the same sign).

Thus, it seems that the main difference now is between the (“extended”) ‘same’ and the ‘different’ items, which seems to make much more sense.

2.4.4.4 MSA

Returning to the Mokken analysis:

Table S13. ‘Flipped’ weird items: MSA: Homogeneity values (with standard errors and 95%CIs) for all items, sorted by item name.

Items	Item H	se	95% ci
Ak1	0.230	(0.030)	[0.172, 0.288]
Ak2	0.244	(0.027)	[0.191, 0.298]
Akt1	0.252	(0.027)	[0.199, 0.305]
Akt2	0.252	(0.027)	[0.199, 0.305]
At1	0.281	(0.025)	[0.233, 0.330]
At2	0.298	(0.027)	[0.246, 0.350]
Atk1	0.212	(0.028)	[0.158, 0.266]
Atk2	0.233	(0.027)	[0.180, 0.286]
Bh1	0.260	(0.024)	[0.213, 0.307]
Bh2	0.263	(0.027)	[0.209, 0.317]
Bhx1	0.154	(0.029)	[0.098, 0.209]
Bhx2	0.147	(0.028)	[0.092, 0.203]
Bs1	0.297	(0.022)	[0.254, 0.340]
Bs2	0.303	(0.023)	[0.258, 0.349]
Bsv1	0.247	(0.026)	[0.196, 0.299]
Bsv2	0.237	(0.028)	[0.182, 0.292]
Bv1	0.241	(0.027)	[0.188, 0.294]
Bv2	0.249	(0.026)	[0.197, 0.300]
Bvs1	0.253	(0.025)	[0.203, 0.302]
Bvs2	0.262	(0.025)	[0.212, 0.312]
Bx1	0.231	(0.028)	[0.176, 0.286]
Bx2	0.307	(0.024)	[0.261, 0.354]
Bxh1	0.211	(0.028)	[0.155, 0.267]
Bxh2	0.133	(0.028)	[0.077, 0.189]
Ch1	0.251	(0.025)	[0.203, 0.299]
Ch2	0.314	(0.022)	[0.272, 0.357]
Chv1	0.167	(0.028)	[0.112, 0.222]
Chv2	0.170	(0.028)	[0.116, 0.224]
Cv1	0.229	(0.027)	[0.176, 0.281]
Cv2	0.246	(0.028)	[0.192, 0.301]
Cvh1	0.230	(0.027)	[0.177, 0.283]
Cvh2	0.231	(0.027)	[0.178, 0.283]
Dc1	0.249	(0.025)	[0.200, 0.299]
Dc2	0.274	(0.027)	[0.221, 0.326]
Dc3	0.307	(0.024)	[0.259, 0.355]
Dc4	0.267	(0.025)	[0.217, 0.316]
Dch1	0.075	(0.035)	[0.006, 0.143]
Dch2	0.200	(0.037)	[0.129, 0.272]
Dcx1	0.193	(0.028)	[0.139, 0.247]
Dcx2	0.182	(0.028)	[0.127, 0.237]
Dh1	0.221	(0.027)	[0.168, 0.273]
Dh2	0.192	(0.031)	[0.131, 0.253]
Dhc1	-0.020	(0.043)	[-0.104, 0.064]
Dhc2	0.056	(0.042)	[-0.026, 0.138]
Dx1	0.233	(0.026)	[0.183, 0.284]
Dx2	0.256	(0.026)	[0.204, 0.308]
Dxc1	0.225	(0.029)	[0.169, 0.281]
Dxc2	0.198	(0.029)	[0.141, 0.254]
Ec1	0.263	(0.025)	[0.213, 0.313]
Ec2	0.266	(0.027)	[0.213, 0.319]
Ec3	0.261	(0.029)	[0.204, 0.319]
Ec4	0.297	(0.024)	[0.249, 0.345]
Ecl1	0.267	(0.026)	[0.217, 0.317]
Ecl2	0.261	(0.025)	[0.211, 0.311]
Ecx1	0.192	(0.029)	[0.136, 0.248]
Ecx2	0.211	(0.029)	[0.155, 0.267]
El1	0.219	(0.030)	[0.160, 0.277]
El2	0.250	(0.026)	[0.200, 0.300]
El3	0.237	(0.028)	[0.183, 0.291]
El4	0.242	(0.028)	[0.187, 0.297]
Elc1	0.292	(0.025)	[0.243, 0.341]
Elc2	0.252	(0.027)	[0.200, 0.305]
Elx1	0.299	(0.026)	[0.249, 0.349]
Elx2	0.300	(0.025)	[0.250, 0.350]
Ex1	0.244	(0.027)	[0.191, 0.297]
Ex2	0.201	(0.026)	[0.149, 0.253]
Ex3	0.263	(0.026)	[0.213, 0.314]
Ex4	0.241	(0.026)	[0.191, 0.292]
Exc1	0.168	(0.029)	[0.112, 0.225]
Exc2	0.165	(0.029)	[0.109, 0.221]
Exl1	0.293	(0.022)	[0.249, 0.337]
Exl2	0.320	(0.024)	[0.272, 0.368]
Fs1	0.301	(0.022)	[0.257, 0.344]
Fs2	0.325	(0.022)	[0.282, 0.369]
Fsv1	0.260	(0.026)	[0.209, 0.311]
Fsv2	0.272	(0.027)	[0.219, 0.324]
Fv1	0.281	(0.024)	[0.234, 0.329]
Fv2	0.291	(0.027)	[0.239, 0.344]
Fvs1	0.291	(0.024)	[0.244, 0.338]
Fvs2	0.238	(0.026)	[0.188, 0.288]
Gt1	0.258	(0.026)	[0.207, 0.310]
Gt2	0.307	(0.026)	[0.255, 0.359]
Gtx1	0.304	(0.020)	[0.265, 0.342]
Gtx2	0.281	(0.025)	[0.232, 0.329]
Gx1	0.189	(0.027)	[0.136, 0.243]
Gx2	0.253	(0.026)	[0.201, 0.304]
Gxt1	0.272	(0.024)	[0.225, 0.318]
Gxt2	0.272	(0.026)	[0.222, 0.323]
Hs1	0.263	(0.028)	[0.209, 0.318]
Hs2	0.309	(0.028)	[0.253, 0.364]
Hst1	0.168	(0.028)	[0.113, 0.223]
Hst2	0.142	(0.028)	[0.088, 0.197]
Ht1	0.267	(0.026)	[0.217, 0.318]
Ht2	0.276	(0.026)	[0.226, 0.326]
Hts1	0.158	(0.029)	[0.101, 0.215]
Hts2	0.150	(0.028)	[0.095, 0.206]
Ic1	0.229	(0.026)	[0.177, 0.281]
Ic2	0.292	(0.025)	[0.244, 0.341]
Icp1	0.188	(0.027)	[0.136, 0.241]
Icp2	0.246	(0.027)	[0.192, 0.299]
Ik1	0.244	(0.027)	[0.192, 0.297]
Ik2	0.259	(0.027)	[0.207, 0.312]
Iks1	0.196	(0.027)	[0.143, 0.250]
Iks2	0.250	(0.027)	[0.197, 0.304]
Ip1	0.165	(0.026)	[0.113, 0.216]
Ip2	0.249	(0.025)	[0.199, 0.298]
Ip3	0.161	(0.030)	[0.102, 0.220]
Ip4	0.248	(0.027)	[0.195, 0.302]
Ip5	0.247	(0.027)	[0.195, 0.300]
Ip6	0.248	(0.026)	[0.197, 0.299]
Ipc1	0.261	(0.024)	[0.214, 0.308]
Ipc2	0.277	(0.026)	[0.226, 0.329]
Ipv1	0.217	(0.031)	[0.156, 0.278]
Ipv2	0.212	(0.031)	[0.151, 0.272]
Ipx1	0.265	(0.024)	[0.218, 0.312]
Ipx2	0.271	(0.025)	[0.221, 0.320]
Is1	0.288	(0.023)	[0.242, 0.334]
Is2	0.276	(0.025)	[0.227, 0.325]
Is3	0.315	(0.024)	[0.268, 0.363]
Is4	0.299	(0.026)	[0.248, 0.349]
Isk1	0.223	(0.027)	[0.170, 0.275]
Isk2	0.236	(0.026)	[0.184, 0.288]
Ist1	0.176	(0.029)	[0.120, 0.233]
Ist2	0.176	(0.029)	[0.120, 0.232]
It1	0.255	(0.027)	[0.202, 0.308]
It2	0.225	(0.028)	[0.170, 0.281]
Its1	0.144	(0.029)	[0.087, 0.200]
Its2	0.204	(0.028)	[0.149, 0.259]
Iv1	0.301	(0.024)	[0.255, 0.347]
Iv2	0.262	(0.025)	[0.213, 0.312]
Ivp1	0.198	(0.029)	[0.141, 0.254]
Ivp2	0.201	(0.030)	[0.142, 0.259]
Ix1	0.219	(0.026)	[0.168, 0.270]
Ix2	0.259	(0.025)	[0.210, 0.309]
Ixp1	0.276	(0.024)	[0.229, 0.324]
Ixp2	0.248	(0.027)	[0.195, 0.300]
Jk1	0.217	(0.026)	[0.167, 0.267]
Jk2	0.271	(0.028)	[0.217, 0.326]
Jkx1	0.281	(0.024)	[0.234, 0.328]
Jkx2	0.258	(0.025)	[0.210, 0.307]
Jx1	0.252	(0.027)	[0.199, 0.306]
Jx2	0.230	(0.027)	[0.177, 0.283]
Jxk1	0.243	(0.026)	[0.192, 0.295]
Jxk2	0.232	(0.027)	[0.180, 0.285]
Kk1	0.272	(0.024)	[0.226, 0.319]
Kk2	0.323	(0.025)	[0.275, 0.372]
Kkl1	-0.063	(0.033)	[-0.127, 0.002]
Kkl2	0.054	(0.033)	[-0.010, 0.118]
Kl1	0.320	(0.030)	[0.260, 0.379]
Kl2	0.334	(0.023)	[0.289, 0.378]
Klk1	0.082	(0.030)	[0.024, 0.141]
Klk2	0.090	(0.030)	[0.031, 0.148]
Lk1	0.269	(0.025)	[0.220, 0.317]
Lk2	0.274	(0.026)	[0.222, 0.325]
Lkl1	0.182	(0.030)	[0.124, 0.240]
Lkl2	0.155	(0.032)	[0.091, 0.218]
Ll1	0.270	(0.027)	[0.218, 0.323]
Ll2	0.229	(0.027)	[0.176, 0.282]
Ll3	0.262	(0.026)	[0.212, 0.312]
Ll4	0.217	(0.028)	[0.161, 0.272]
Llk1	0.120	(0.034)	[0.055, 0.186]
Llk2	0.138	(0.034)	[0.071, 0.206]
Llp1	0.123	(0.029)	[0.066, 0.181]
Llp2	0.043	(0.029)	[-0.013, 0.099]
Lp1	0.228	(0.026)	[0.176, 0.279]
Lp2	0.255	(0.028)	[0.201, 0.309]
Lp3	0.278	(0.025)	[0.229, 0.326]
Lp4	0.300	(0.027)	[0.248, 0.353]
Lpl1	0.098	(0.030)	[0.040, 0.157]
Lpl2	0.114	(0.030)	[0.056, 0.172]
Lpv1	0.161	(0.030)	[0.101, 0.221]
Lpv2	0.172	(0.030)	[0.113, 0.231]
Lv1	0.225	(0.028)	[0.171, 0.280]
Lv2	0.238	(0.026)	[0.187, 0.290]
Lvp1	0.169	(0.029)	[0.113, 0.226]
Lvp2	0.168	(0.029)	[0.111, 0.226]
Mc1	0.122	(0.030)	[0.064, 0.180]
Mc2	0.098	(0.030)	[0.039, 0.156]
Mc3	0.130	(0.029)	[0.073, 0.187]
Mc4	0.030	(0.027)	[-0.023, 0.083]
Mch1	0.091	(0.033)	[0.026, 0.155]
Mch2	0.179	(0.033)	[0.114, 0.244]
Mcl1	0.287	(0.023)	[0.242, 0.332]
Mcl2	0.283	(0.024)	[0.236, 0.329]
Mh1	0.197	(0.027)	[0.144, 0.249]
Mh2	0.235	(0.026)	[0.184, 0.287]
Mh3	0.241	(0.025)	[0.192, 0.290]
Mh4	0.237	(0.026)	[0.185, 0.288]
Mhc1	0.138	(0.035)	[0.070, 0.206]
Mhc2	0.172	(0.035)	[0.102, 0.241]
Mhl1	0.256	(0.024)	[0.208, 0.304]
Mhl2	0.297	(0.024)	[0.249, 0.345]
Ml1	0.267	(0.026)	[0.217, 0.318]
Ml2	0.239	(0.028)	[0.185, 0.293]
Ml3	0.236	(0.027)	[0.183, 0.290]
Ml4	0.191	(0.033)	[0.127, 0.256]
Mlc1	0.307	(0.024)	[0.261, 0.353]
Mlc2	0.273	(0.023)	[0.227, 0.318]
Mlh1	0.261	(0.024)	[0.213, 0.308]
Mlh2	0.307	(0.023)	[0.261, 0.353]
Nk1	0.264	(0.024)	[0.217, 0.312]
Nk2	0.269	(0.027)	[0.216, 0.321]
Nkp1	0.183	(0.029)	[0.126, 0.241]
Nkp2	0.197	(0.030)	[0.138, 0.256]
Np1	0.270	(0.025)	[0.221, 0.319]
Np2	0.263	(0.033)	[0.199, 0.328]
Npk1	0.152	(0.031)	[0.091, 0.212]
Npk2	0.169	(0.033)	[0.104, 0.234]

Table S14. Same table as bove but sorted by homogeneity.

Items	Item H	se	95% ci
Kl2	0.334	(0.023)	[0.289, 0.378]
Fs2	0.325	(0.022)	[0.282, 0.369]
Kk2	0.323	(0.025)	[0.275, 0.372]
Exl2	0.320	(0.024)	[0.272, 0.368]
Kl1	0.320	(0.030)	[0.260, 0.379]
Is3	0.315	(0.024)	[0.268, 0.363]
Ch2	0.314	(0.022)	[0.272, 0.357]
Hs2	0.309	(0.028)	[0.253, 0.364]
Bx2	0.307	(0.024)	[0.261, 0.354]
Dc3	0.307	(0.024)	[0.259, 0.355]
Gt2	0.307	(0.026)	[0.255, 0.359]
Mlc1	0.307	(0.024)	[0.261, 0.353]
Mlh2	0.307	(0.023)	[0.261, 0.353]
Gtx1	0.304	(0.020)	[0.265, 0.342]
Bs2	0.303	(0.023)	[0.258, 0.349]
Fs1	0.301	(0.022)	[0.257, 0.344]
Iv1	0.301	(0.024)	[0.255, 0.347]
Elx2	0.300	(0.025)	[0.250, 0.350]
Lp4	0.300	(0.027)	[0.248, 0.353]
Elx1	0.299	(0.026)	[0.249, 0.349]
Is4	0.299	(0.026)	[0.248, 0.349]
At2	0.298	(0.027)	[0.246, 0.350]
Bs1	0.297	(0.022)	[0.254, 0.340]
Ec4	0.297	(0.024)	[0.249, 0.345]
Mhl2	0.297	(0.024)	[0.249, 0.345]
Exl1	0.293	(0.022)	[0.249, 0.337]
Elc1	0.292	(0.025)	[0.243, 0.341]
Ic2	0.292	(0.025)	[0.244, 0.341]
Fv2	0.291	(0.027)	[0.239, 0.344]
Fvs1	0.291	(0.024)	[0.244, 0.338]
Is1	0.288	(0.023)	[0.242, 0.334]
Mcl1	0.287	(0.023)	[0.242, 0.332]
Mcl2	0.283	(0.024)	[0.236, 0.329]
At1	0.281	(0.025)	[0.233, 0.330]
Fv1	0.281	(0.024)	[0.234, 0.329]
Gtx2	0.281	(0.025)	[0.232, 0.329]
Jkx1	0.281	(0.024)	[0.234, 0.328]
Lp3	0.278	(0.025)	[0.229, 0.326]
Ipc2	0.277	(0.026)	[0.226, 0.329]
Ht2	0.276	(0.026)	[0.226, 0.326]
Is2	0.276	(0.025)	[0.227, 0.325]
Ixp1	0.276	(0.024)	[0.229, 0.324]
Dc2	0.274	(0.027)	[0.221, 0.326]
Lk2	0.274	(0.026)	[0.222, 0.325]
Mlc2	0.273	(0.023)	[0.227, 0.318]
Fsv2	0.272	(0.027)	[0.219, 0.324]
Gxt1	0.272	(0.024)	[0.225, 0.318]
Gxt2	0.272	(0.026)	[0.222, 0.323]
Kk1	0.272	(0.024)	[0.226, 0.319]
Ipx2	0.271	(0.025)	[0.221, 0.320]
Jk2	0.271	(0.028)	[0.217, 0.326]
Ll1	0.270	(0.027)	[0.218, 0.323]
Np1	0.270	(0.025)	[0.221, 0.319]
Lk1	0.269	(0.025)	[0.220, 0.317]
Nk2	0.269	(0.027)	[0.216, 0.321]
Dc4	0.267	(0.025)	[0.217, 0.316]
Ecl1	0.267	(0.026)	[0.217, 0.317]
Ht1	0.267	(0.026)	[0.217, 0.318]
Ml1	0.267	(0.026)	[0.217, 0.318]
Ec2	0.266	(0.027)	[0.213, 0.319]
Ipx1	0.265	(0.024)	[0.218, 0.312]
Nk1	0.264	(0.024)	[0.217, 0.312]
Bh2	0.263	(0.027)	[0.209, 0.317]
Ec1	0.263	(0.025)	[0.213, 0.313]
Ex3	0.263	(0.026)	[0.213, 0.314]
Hs1	0.263	(0.028)	[0.209, 0.318]
Np2	0.263	(0.033)	[0.199, 0.328]
Bvs2	0.262	(0.025)	[0.212, 0.312]
Iv2	0.262	(0.025)	[0.213, 0.312]
Ll3	0.262	(0.026)	[0.212, 0.312]
Ec3	0.261	(0.029)	[0.204, 0.319]
Ecl2	0.261	(0.025)	[0.211, 0.311]
Ipc1	0.261	(0.024)	[0.214, 0.308]
Mlh1	0.261	(0.024)	[0.213, 0.308]
Bh1	0.260	(0.024)	[0.213, 0.307]
Fsv1	0.260	(0.026)	[0.209, 0.311]
Ik2	0.259	(0.027)	[0.207, 0.312]
Ix2	0.259	(0.025)	[0.210, 0.309]
Gt1	0.258	(0.026)	[0.207, 0.310]
Jkx2	0.258	(0.025)	[0.210, 0.307]
Dx2	0.256	(0.026)	[0.204, 0.308]
Mhl1	0.256	(0.024)	[0.208, 0.304]
It1	0.255	(0.027)	[0.202, 0.308]
Lp2	0.255	(0.028)	[0.201, 0.309]
Bvs1	0.253	(0.025)	[0.203, 0.302]
Gx2	0.253	(0.026)	[0.201, 0.304]
Akt1	0.252	(0.027)	[0.199, 0.305]
Akt2	0.252	(0.027)	[0.199, 0.305]
Elc2	0.252	(0.027)	[0.200, 0.305]
Jx1	0.252	(0.027)	[0.199, 0.306]
Ch1	0.251	(0.025)	[0.203, 0.299]
El2	0.250	(0.026)	[0.200, 0.300]
Iks2	0.250	(0.027)	[0.197, 0.304]
Bv2	0.249	(0.026)	[0.197, 0.300]
Dc1	0.249	(0.025)	[0.200, 0.299]
Ip2	0.249	(0.025)	[0.199, 0.298]
Ip4	0.248	(0.027)	[0.195, 0.302]
Ip6	0.248	(0.026)	[0.197, 0.299]
Ixp2	0.248	(0.027)	[0.195, 0.300]
Bsv1	0.247	(0.026)	[0.196, 0.299]
Ip5	0.247	(0.027)	[0.195, 0.300]
Cv2	0.246	(0.028)	[0.192, 0.301]
Icp2	0.246	(0.027)	[0.192, 0.299]
Ak2	0.244	(0.027)	[0.191, 0.298]
Ex1	0.244	(0.027)	[0.191, 0.297]
Ik1	0.244	(0.027)	[0.192, 0.297]
Jxk1	0.243	(0.026)	[0.192, 0.295]
El4	0.242	(0.028)	[0.187, 0.297]
Bv1	0.241	(0.027)	[0.188, 0.294]
Ex4	0.241	(0.026)	[0.191, 0.292]
Mh3	0.241	(0.025)	[0.192, 0.290]
Ml2	0.239	(0.028)	[0.185, 0.293]
Fvs2	0.238	(0.026)	[0.188, 0.288]
Lv2	0.238	(0.026)	[0.187, 0.290]
Bsv2	0.237	(0.028)	[0.182, 0.292]
El3	0.237	(0.028)	[0.183, 0.291]
Mh4	0.237	(0.026)	[0.185, 0.288]
Isk2	0.236	(0.026)	[0.184, 0.288]
Ml3	0.236	(0.027)	[0.183, 0.290]
Mh2	0.235	(0.026)	[0.184, 0.287]
Atk2	0.233	(0.027)	[0.180, 0.286]
Dx1	0.233	(0.026)	[0.183, 0.284]
Jxk2	0.232	(0.027)	[0.180, 0.285]
Bx1	0.231	(0.028)	[0.176, 0.286]
Cvh2	0.231	(0.027)	[0.178, 0.283]
Ak1	0.230	(0.030)	[0.172, 0.288]
Cvh1	0.230	(0.027)	[0.177, 0.283]
Jx2	0.230	(0.027)	[0.177, 0.283]
Cv1	0.229	(0.027)	[0.176, 0.281]
Ic1	0.229	(0.026)	[0.177, 0.281]
Ll2	0.229	(0.027)	[0.176, 0.282]
Lp1	0.228	(0.026)	[0.176, 0.279]
Dxc1	0.225	(0.029)	[0.169, 0.281]
It2	0.225	(0.028)	[0.170, 0.281]
Lv1	0.225	(0.028)	[0.171, 0.280]
Isk1	0.223	(0.027)	[0.170, 0.275]
Dh1	0.221	(0.027)	[0.168, 0.273]
El1	0.219	(0.030)	[0.160, 0.277]
Ix1	0.219	(0.026)	[0.168, 0.270]
Ipv1	0.217	(0.031)	[0.156, 0.278]
Jk1	0.217	(0.026)	[0.167, 0.267]
Ll4	0.217	(0.028)	[0.161, 0.272]
Atk1	0.212	(0.028)	[0.158, 0.266]
Ipv2	0.212	(0.031)	[0.151, 0.272]
Bxh1	0.211	(0.028)	[0.155, 0.267]
Ecx2	0.211	(0.029)	[0.155, 0.267]
Its2	0.204	(0.028)	[0.149, 0.259]
Ex2	0.201	(0.026)	[0.149, 0.253]
Ivp2	0.201	(0.030)	[0.142, 0.259]
Dch2	0.200	(0.037)	[0.129, 0.272]
Dxc2	0.198	(0.029)	[0.141, 0.254]
Ivp1	0.198	(0.029)	[0.141, 0.254]
Mh1	0.197	(0.027)	[0.144, 0.249]
Nkp2	0.197	(0.030)	[0.138, 0.256]
Iks1	0.196	(0.027)	[0.143, 0.250]
Dcx1	0.193	(0.028)	[0.139, 0.247]
Dh2	0.192	(0.031)	[0.131, 0.253]
Ecx1	0.192	(0.029)	[0.136, 0.248]
Ml4	0.191	(0.033)	[0.127, 0.256]
Gx1	0.189	(0.027)	[0.136, 0.243]
Icp1	0.188	(0.027)	[0.136, 0.241]
Nkp1	0.183	(0.029)	[0.126, 0.241]
Dcx2	0.182	(0.028)	[0.127, 0.237]
Lkl1	0.182	(0.030)	[0.124, 0.240]
Mch2	0.179	(0.033)	[0.114, 0.244]
Ist1	0.176	(0.029)	[0.120, 0.233]
Ist2	0.176	(0.029)	[0.120, 0.232]
Lpv2	0.172	(0.030)	[0.113, 0.231]
Mhc2	0.172	(0.035)	[0.102, 0.241]
Chv2	0.170	(0.028)	[0.116, 0.224]
Lvp1	0.169	(0.029)	[0.113, 0.226]
Npk2	0.169	(0.033)	[0.104, 0.234]
Exc1	0.168	(0.029)	[0.112, 0.225]
Hst1	0.168	(0.028)	[0.113, 0.223]
Lvp2	0.168	(0.029)	[0.111, 0.226]
Chv1	0.167	(0.028)	[0.112, 0.222]
Exc2	0.165	(0.029)	[0.109, 0.221]
Ip1	0.165	(0.026)	[0.113, 0.216]
Ip3	0.161	(0.030)	[0.102, 0.220]
Lpv1	0.161	(0.030)	[0.101, 0.221]
Hts1	0.158	(0.029)	[0.101, 0.215]
Lkl2	0.155	(0.032)	[0.091, 0.218]
Bhx1	0.154	(0.029)	[0.098, 0.209]
Npk1	0.152	(0.031)	[0.091, 0.212]
Hts2	0.150	(0.028)	[0.095, 0.206]
Bhx2	0.147	(0.028)	[0.092, 0.203]
Its1	0.144	(0.029)	[0.087, 0.200]
Hst2	0.142	(0.028)	[0.088, 0.197]
Llk2	0.138	(0.034)	[0.071, 0.206]
Mhc1	0.138	(0.035)	[0.070, 0.206]
Bxh2	0.133	(0.028)	[0.077, 0.189]
Mc3	0.130	(0.029)	[0.073, 0.187]
Llp1	0.123	(0.029)	[0.066, 0.181]
Mc1	0.122	(0.030)	[0.064, 0.180]
Llk1	0.120	(0.034)	[0.055, 0.186]
Lpl2	0.114	(0.030)	[0.056, 0.172]
Lpl1	0.098	(0.030)	[0.040, 0.157]
Mc2	0.098	(0.030)	[0.039, 0.156]
Mch1	0.091	(0.033)	[0.026, 0.155]
Klk2	0.090	(0.030)	[0.031, 0.148]
Klk1	0.082	(0.030)	[0.024, 0.141]
Dch1	0.075	(0.035)	[0.006, 0.143]
Dhc2	0.056	(0.042)	[-0.026, 0.138]
Kkl2	0.054	(0.033)	[-0.010, 0.118]
Llp2	0.043	(0.029)	[-0.013, 0.099]
Mc4	0.030	(0.027)	[-0.023, 0.083]
Dhc1	-0.020	(0.043)	[-0.104, 0.064]
Kkl1	-0.063	(0.033)	[-0.127, 0.002]

The complete item set has a homogeneity value H (se, 95%CI) of 0.223, (0.008), [0.207, 0.238]: this is significantly lower than the recommended 0.30, suggesting that the scale is not homogeneous. This is further supported by the fact that few items have a homogeneity around or above this value (only 19 if we consider the point estimate, and 93 out of 208 if we consider a 95%CI with an upper limit above 0.3). Interestingly, the homogeneity of related items (different presentations and different orders of the tones) are overall very similar, suggesting again that this an intrinsic property of the segments and tone(s) and not of their repeated presentation of order of tones.

Table S15. ‘Flipped’ weird items: MSA: aisp for increasing H thresholds (c) for all items.

Items	c=0.05	c=0.10	c=0.15	c=0.20	c=0.25	c=0.30	c=0.35	c=0.40	c=0.45	c=0.50	c=0.55	c=0.60
Ak1	1	1	1	1	1	9	25	0	0	0	0	0
Ak2	1	1	1	1	1	1	7	15	23	0	0	0
Akt1	1	1	1	1	1	2	2	12	21	29	0	0
Akt2	1	1	1	1	1	2	2	47	0	0	0	0
At1	1	1	1	1	1	1	1	0	0	0	0	0
At2	1	1	1	1	1	1	1	7	12	17	0	0
Atk1	1	1	1	1	1	2	2	43	0	0	0	0
Atk2	1	1	1	1	1	2	2	0	0	0	0	0
Bh1	1	1	1	1	1	1	1	8	0	0	0	0
Bh2	1	1	1	1	1	1	11	24	36	0	0	0
Bhx1	2	2	2	7	5	6	5	40	0	0	0	0
Bhx2	2	2	2	7	4	5	12	25	39	0	0	0
Bs1	1	1	1	1	1	1	1	1	1	0	0	0
Bs2	1	1	1	1	1	1	1	1	1	14	14	0
Bsv1	1	1	1	1	1	2	4	18	31	0	0	0
Bsv2	1	1	1	1	1	2	2	14	20	0	0	0
Bv1	1	1	1	1	1	1	5	10	15	23	0	0
Bv2	1	1	1	1	1	1	17	35	0	0	0	0
Bvs1	1	1	1	1	1	2	2	23	35	0	0	0
Bvs2	1	1	1	1	1	2	2	14	20	27	0	0
Bx1	1	1	1	1	1	1	11	24	42	0	0	0
Bx2	1	1	1	1	1	1	1	1	1	1	1	1
Bxh1	1	1	1	1	1	1	11	26	41	0	0	0
Bxh2	0	0	6	0	0	0	17	0	0	0	0	0
Ch1	1	1	1	1	1	1	5	33	0	0	0	0
Ch2	1	1	1	1	1	1	1	1	9	12	12	0
Chv1	1	1	1	3	9	14	0	0	0	0	0	0
Chv2	3	3	3	3	9	0	13	0	0	0	0	0
Cv1	1	1	1	1	1	12	30	0	0	0	0	0
Cv2	1	1	1	1	1	1	16	32	0	0	0	0
Cvh1	1	1	1	1	1	2	21	46	0	0	0	0
Cvh2	1	1	1	1	1	2	13	28	0	0	0	0
Dc1	1	1	1	1	1	0	30	0	0	0	0	0
Dc2	1	1	1	1	1	1	1	8	11	19	0	0
Dc3	1	1	1	1	1	1	1	1	28	0	0	0
Dc4	1	1	1	1	1	1	5	36	0	0	0	0
Dch1	3	3	3	5	0	16	0	0	0	0	0	0
Dch2	3	3	3	3	3	2	14	6	20	27	0	0
Dcx1	1	1	1	1	3	2	14	29	0	0	0	0
Dcx2	3	3	3	3	3	8	14	29	0	0	0	0
Dh1	1	1	1	1	1	6	0	0	40	0	0	0
Dh2	2	2	2	2	2	3	3	3	4	6	6	0
Dhc1	3	3	5	5	0	0	0	0	0	0	0	0
Dhc2	0	0	5	5	7	16	0	0	0	0	0	0
Dx1	1	1	1	1	1	1	20	41	16	24	0	0
Dx2	1	1	1	1	1	1	22	11	28	0	0	0
Dxc1	3	3	3	3	3	2	23	20	6	8	8	0
Dxc2	1	1	1	1	0	2	0	0	0	0	0	0
Ec1	1	1	1	1	1	1	10	27	0	0	0	0
Ec2	1	1	1	1	1	1	1	8	33	0	0	0
Ec3	1	1	1	1	1	1	8	4	27	0	0	0
Ec4	1	1	1	1	1	1	1	1	1	4	4	4
Ecl1	1	1	1	1	1	2	2	31	0	0	0	0
Ecl2	1	1	1	1	1	2	2	23	0	0	0	0
Ecx1	3	3	3	3	14	0	0	38	24	0	0	0
Ecx2	1	1	1	1	1	2	14	0	17	10	10	0
El1	1	1	1	1	4	13	20	41	0	0	0	0
El2	1	1	1	1	1	1	10	0	0	0	0	0
El3	1	1	1	1	1	1	26	36	0	0	0	0
El4	1	1	1	1	1	1	5	36	0	0	0	0
Elc1	1	1	1	1	1	2	2	2	7	0	0	0
Elc2	1	1	1	1	1	2	2	2	37	0	0	0
Elx1	1	1	1	1	1	1	1	2	3	5	0	0
Elx2	1	1	1	1	1	1	2	2	3	5	5	0
Ex1	1	1	1	1	1	1	1	8	13	4	4	4
Ex2	1	1	1	1	5	6	0	26	41	0	0	0
Ex3	1	1	1	1	1	1	1	1	1	2	2	2
Ex4	1	1	1	1	1	1	11	24	36	0	0	0
Exc1	3	3	3	3	14	0	0	0	0	0	0	0
Exc2	3	3	3	3	3	8	4	18	31	0	0	0
Exl1	1	1	1	1	1	2	2	2	3	28	0	0
Exl2	1	1	1	1	1	1	1	2	3	20	0	0
Fs1	1	1	1	1	1	1	1	1	16	24	0	0
Fs2	1	1	1	1	1	1	1	1	10	0	0	0
Fsv1	1	1	1	1	1	2	2	22	34	0	0	0
Fsv2	1	1	1	1	1	2	2	2	37	0	0	0
Fv1	1	1	1	1	1	1	1	7	9	17	0	0
Fv2	1	1	1	1	1	1	2	31	8	11	11	0
Fvs1	1	1	1	1	1	2	2	2	3	0	0	0
Fvs2	1	1	1	1	1	2	2	12	22	30	0	0
Gt1	1	1	1	1	1	1	5	10	40	0	0	0
Gt2	1	1	1	1	1	1	1	1	1	14	14	0
Gtx1	1	1	1	1	1	2	2	2	3	5	5	0
Gtx2	1	1	1	1	1	2	2	2	2	3	3	3
Gx1	1	1	1	1	5	0	6	13	26	31	0	0
Gx2	1	1	1	1	1	1	1	8	11	19	0	0
Gxt1	1	1	1	1	1	2	2	2	2	10	10	0
Gxt2	1	1	1	1	1	2	2	12	21	29	0	0
Hs1	1	1	1	1	1	1	6	13	13	31	0	0
Hs2	1	1	1	1	1	1	1	1	10	13	13	0
Hst1	2	2	2	4	12	0	0	0	0	0	0	0
Hst2	2	2	2	4	12	20	0	0	0	0	0	0
Ht1	1	1	1	1	1	1	11	34	0	0	0	0
Ht2	1	1	1	1	1	1	10	4	5	7	7	0
Hts1	2	2	2	0	12	9	25	0	0	0	0	0
Hts2	2	2	6	4	12	20	0	0	0	0	0	0
Ic1	1	1	1	1	1	1	0	0	33	0	0	0
Ic2	1	1	1	1	1	1	1	1	1	0	0	0
Icp1	3	3	3	3	3	17	15	30	0	0	0	0
Icp2	1	1	1	1	1	2	23	0	0	0	0	0
Ik1	1	1	1	1	1	18	0	0	0	0	0	0
Ik2	1	1	1	1	1	1	7	33	0	0	0	0
Iks1	3	3	3	3	3	17	0	0	0	0	0	0
Iks2	1	1	1	1	1	2	2	2	0	0	0	0
Ip1	1	1	1	8	13	0	0	0	0	0	0	0
Ip2	1	1	1	1	1	1	1	45	0	0	0	0
Ip3	2	2	4	6	0	0	0	0	0	0	0	0
Ip4	1	1	1	1	1	1	6	7	26	0	0	0
Ip5	1	1	1	1	1	1	8	42	0	0	0	0
Ip6	1	1	1	1	1	1	16	32	0	0	0	0
Ipc1	1	1	1	1	1	2	2	12	22	30	0	0
Ipc2	1	1	1	1	1	2	2	2	2	18	0	0
Ipv1	1	1	1	1	1	2	2	5	7	9	9	0
Ipv2	1	1	1	1	1	2	2	2	2	3	3	3
Ipx1	1	1	1	1	1	2	2	5	7	9	9	0
Ipx2	1	1	1	1	1	2	2	20	0	0	0	0
Is1	1	1	1	1	1	1	1	6	9	15	15	0
Is2	1	1	1	1	1	1	9	0	0	0	0	0
Is3	1	1	1	1	1	1	1	4	5	7	7	0
Is4	1	1	1	1	1	1	1	6	9	15	15	0
Isk1	1	1	1	1	1	14	19	39	0	0	0	0
Isk2	1	1	1	1	1	2	23	43	0	0	0	0
Ist1	2	2	7	0	11	18	10	0	0	0	0	0
Ist2	2	2	2	7	4	19	0	42	0	0	0	0
It1	1	1	1	1	1	1	7	15	23	0	0	0
It2	1	1	1	1	1	0	0	0	0	0	0	0
Its1	2	2	6	0	0	0	0	0	0	0	0	0
Its2	1	1	1	1	4	13	8	27	0	0	0	0
Iv1	1	1	1	1	1	1	1	1	30	16	16	0
Iv2	1	1	1	1	1	1	8	21	27	0	0	0
Ivp1	1	1	1	1	1	2	15	30	0	0	0	0
Ivp2	1	1	1	1	1	2	2	23	35	0	0	0
Ix1	1	1	1	1	4	5	12	25	39	0	0	0
Ix2	1	1	1	1	1	6	17	35	0	20	0	0
Ixp1	1	1	1	1	1	2	2	5	17	0	0	0
Ixp2	1	1	1	1	1	2	2	17	29	0	0	0
Jk1	1	1	1	1	11	0	7	15	0	0	0	0
Jk2	1	1	1	1	1	1	24	0	0	0	0	0
Jkx1	1	1	1	1	1	2	2	2	24	0	0	0
Jkx2	1	1	1	1	1	2	2	38	0	0	0	0
Jx1	1	1	1	1	1	1	1	4	5	16	16	0
Jx2	1	1	1	1	1	19	0	0	0	0	0	0
Jxk1	1	1	1	1	1	2	4	9	14	22	0	0
Jxk2	1	1	1	1	1	2	2	43	0	0	0	0
Kk1	1	1	1	1	1	1	1	8	0	0	0	0
Kk2	1	1	1	1	1	1	1	1	1	2	2	2
Kkl1	0	0	7	4	8	15	0	0	0	0	0	0
Kkl2	2	2	2	4	8	15	26	0	0	0	0	0
Kl1	1	1	1	1	1	1	1	1	9	12	12	0
Kl2	1	1	1	1	1	1	1	1	1	1	1	1
Klk1	2	2	7	4	15	12	28	0	0	0	0	0
Klk2	2	2	2	4	15	0	24	44	0	0	0	0
Lk1	1	1	1	1	1	1	10	4	30	0	0	0
Lk2	1	1	1	1	1	1	1	1	10	13	13	0
Lkl1	1	1	1	1	6	10	29	0	25	0	0	0
Lkl2	3	3	3	3	6	10	29	0	0	0	0	0
Ll1	1	1	1	1	1	1	1	11	19	26	0	0
Ll2	1	1	1	1	1	1	8	7	0	0	0	0
Ll3	1	1	1	1	1	1	1	21	27	0	0	0
Ll4	1	1	1	1	4	12	28	0	0	0	0	0
Llk1	3	3	3	0	6	0	0	0	0	0	0	0
Llk2	3	3	3	3	6	10	0	47	0	0	0	0
Llp1	2	2	4	6	10	11	22	0	0	0	0	0
Llp2	2	2	4	6	10	0	0	0	0	0	0	0
Lp1	1	1	1	1	1	19	0	0	0	0	0	0
Lp2	1	1	1	1	1	1	1	16	0	0	0	0
Lp3	1	1	1	1	1	1	1	16	12	25	0	0
Lp4	1	1	1	1	1	1	1	1	8	12	0	0
Lpl1	2	2	4	8	13	0	0	34	0	0	0	0
Lpl2	2	2	4	6	0	0	0	0	0	0	0	0
Lpv1	3	3	3	3	3	10	27	0	0	0	0	0
Lpv2	3	3	3	3	3	2	27	17	29	0	0	0
Lv1	1	1	1	1	1	1	5	10	15	23	0	0
Lv2	1	1	1	1	1	1	5	40	0	0	0	0
Lvp1	3	3	3	3	3	10	27	0	0	0	0	0
Lvp2	3	3	3	3	3	2	13	28	0	0	0	0
Mc1	2	2	2	2	2	3	0	0	0	0	0	0
Mc2	2	2	2	2	2	3	3	3	4	6	6	0
Mc3	2	2	2	2	2	7	0	0	0	0	0	0
Mc4	0	0	2	2	0	0	0	0	0	0	0	0
Mch1	3	3	3	9	7	0	0	0	0	0	0	0
Mch2	3	3	3	3	7	8	4	9	14	22	0	0
Mcl1	1	1	1	1	1	2	2	2	2	21	0	0
Mcl2	1	1	1	1	1	2	2	2	2	18	0	0
Mh1	1	1	1	1	2	7	18	37	0	0	0	0
Mh2	1	1	1	1	1	1	7	0	0	0	0	0
Mh3	1	1	1	1	1	7	18	37	0	0	0	0
Mh4	1	1	1	1	1	5	18	45	0	0	0	0
Mhc1	3	3	3	9	7	0	0	0	38	0	0	0
Mhc2	3	3	3	3	7	8	0	22	34	28	0	0
Mhl1	1	1	1	1	1	2	2	14	25	0	0	0
Mhl2	1	1	1	1	1	2	2	2	2	18	0	0
Ml1	1	1	1	1	1	1	1	1	12	25	0	0
Ml2	1	1	1	1	1	1	7	44	42	0	0	0
Ml3	1	1	1	1	1	3	17	0	0	0	0	0
Ml4	1	1	1	1	4	11	0	0	0	0	0	0
Mlc1	1	1	1	1	1	1	1	2	8	11	11	0
Mlc2	1	1	1	1	1	2	2	2	6	8	8	0
Mlh1	1	1	1	1	1	2	2	20	38	0	0	0
Mlh2	1	1	1	1	1	1	2	2	18	21	0	0
Nk1	1	1	1	1	1	1	6	0	0	0	0	0
Nk2	1	1	1	1	1	1	19	39	0	0	0	0
Nkp1	1	1	1	1	3	0	0	0	18	0	0	0
Nkp2	3	3	3	3	3	2	21	46	0	0	0	0
Np1	1	1	1	1	1	1	1	1	16	2	0	0
Np2	1	1	1	1	1	1	8	11	19	26	0	0
Npk1	3	3	3	3	3	4	9	19	32	0	0	0
Npk2	3	3	3	3	3	4	9	19	32	0	0	0

The results are slightly better, but still far from ideal for IRT…

2.4.4.5 Conclusions

So, it seems that the “weird” stimuli Bhx, Hst, Ist, Kkl1 and Llp (all presentations and variants) are perceived by the participants as (rather difficult) ‘same’-type items and not as the intended ‘different’-type items. With this change, the items seem to fall into the two natural classes ‘same’ vs ‘different’ (even if there is a lot of unaccounted variation).

2.4.5 Let’s reduce the items set

The MSA suggests that there are too many items, and it seems clear that the successive presentations of the same item do not seem to make a difference and, for the (by design) ‘different’ items, the order of the tones does not seem to mater as well. If this is so, we can reduce the items set by:

• keeping systematically the 2^nd presentation,
• and, for the ‘different’ items, only one order of tones (say, the 1^st in alphabetical order).

With these:

Table S16. ‘Flipped’ weird items: MSA: Homogeneity values (with standard errors and 95%CIs) for all items, sorted by item name.

Items	Item H	se	95% ci
Ak1	0.240	(0.029)	[0.182, 0.297]
Akt1	0.237	(0.029)	[0.180, 0.293]
At1	0.276	(0.025)	[0.228, 0.324]
Bh1	0.273	(0.025)	[0.224, 0.322]
Bhx1	0.175	(0.031)	[0.114, 0.236]
Bs1	0.305	(0.022)	[0.262, 0.347]
Bsv1	0.239	(0.027)	[0.185, 0.293]
Bv1	0.257	(0.029)	[0.201, 0.313]
Bx1	0.248	(0.030)	[0.190, 0.306]
Ch1	0.272	(0.025)	[0.223, 0.321]
Chv1	0.153	(0.030)	[0.095, 0.211]
Cv1	0.243	(0.026)	[0.193, 0.294]
Dc1	0.257	(0.026)	[0.207, 0.307]
Dch1	0.062	(0.039)	[-0.015, 0.138]
Dcx1	0.173	(0.029)	[0.117, 0.229]
Dh1	0.230	(0.027)	[0.176, 0.284]
Dx1	0.251	(0.028)	[0.196, 0.306]
Ec1	0.274	(0.027)	[0.221, 0.328]
Ecl1	0.254	(0.027)	[0.200, 0.307]
Ecx1	0.185	(0.031)	[0.125, 0.246]
El1	0.231	(0.031)	[0.169, 0.292]
Elx1	0.281	(0.028)	[0.227, 0.335]
Ex1	0.263	(0.029)	[0.207, 0.319]
Fs1	0.321	(0.024)	[0.275, 0.368]
Fsv1	0.236	(0.027)	[0.184, 0.288]
Fv1	0.295	(0.025)	[0.246, 0.344]
Gt1	0.276	(0.027)	[0.222, 0.329]
Gtx1	0.290	(0.021)	[0.248, 0.331]
Gx1	0.205	(0.030)	[0.147, 0.263]
Hs1	0.300	(0.028)	[0.245, 0.354]
Hst1	0.185	(0.029)	[0.127, 0.242]
Ht1	0.272	(0.027)	[0.219, 0.324]
Ic1	0.240	(0.028)	[0.186, 0.295]
Icp1	0.184	(0.028)	[0.129, 0.239]
Ik1	0.257	(0.030)	[0.197, 0.317]
Iks1	0.191	(0.029)	[0.136, 0.247]
Ip1	0.175	(0.027)	[0.121, 0.228]
Ipv1	0.211	(0.035)	[0.143, 0.279]
Ipx1	0.249	(0.025)	[0.200, 0.298]
Is1	0.292	(0.024)	[0.246, 0.339]
Ist1	0.199	(0.031)	[0.139, 0.259]
It1	0.253	(0.029)	[0.196, 0.310]
Iv1	0.309	(0.024)	[0.261, 0.357]
Ix1	0.231	(0.027)	[0.179, 0.283]
Jk1	0.220	(0.027)	[0.168, 0.273]
Jkx1	0.265	(0.025)	[0.215, 0.314]
Jx1	0.272	(0.029)	[0.214, 0.329]
Kk1	0.285	(0.027)	[0.232, 0.337]
Kkl1	-0.033	(0.038)	[-0.108, 0.041]
Kl1	0.327	(0.033)	[0.263, 0.392]
Lk1	0.272	(0.025)	[0.223, 0.322]
Lkl1	0.174	(0.032)	[0.111, 0.237]
Ll1	0.277	(0.028)	[0.222, 0.332]
Llp1	0.132	(0.032)	[0.069, 0.195]
Lp1	0.245	(0.027)	[0.192, 0.298]
Lpv1	0.157	(0.034)	[0.091, 0.224]
Lv1	0.247	(0.029)	[0.190, 0.303]
Mc1	0.145	(0.033)	[0.080, 0.210]
Mch1	0.071	(0.037)	[-0.002, 0.143]
Mcl1	0.277	(0.023)	[0.232, 0.323]
Mh1	0.212	(0.028)	[0.156, 0.267]
Mhl1	0.244	(0.025)	[0.195, 0.293]
Ml1	0.270	(0.028)	[0.216, 0.324]
Nk1	0.274	(0.026)	[0.223, 0.325]
Nkp1	0.189	(0.032)	[0.126, 0.251]
Np1	0.281	(0.025)	[0.231, 0.331]

Table S17. Same table as bove but sorted by homogeneity.

Items	Item H	se	95% ci
Kl1	0.327	(0.033)	[0.263, 0.392]
Fs1	0.321	(0.024)	[0.275, 0.368]
Iv1	0.309	(0.024)	[0.261, 0.357]
Bs1	0.305	(0.022)	[0.262, 0.347]
Hs1	0.300	(0.028)	[0.245, 0.354]
Fv1	0.295	(0.025)	[0.246, 0.344]
Is1	0.292	(0.024)	[0.246, 0.339]
Gtx1	0.290	(0.021)	[0.248, 0.331]
Kk1	0.285	(0.027)	[0.232, 0.337]
Elx1	0.281	(0.028)	[0.227, 0.335]
Np1	0.281	(0.025)	[0.231, 0.331]
Ll1	0.277	(0.028)	[0.222, 0.332]
Mcl1	0.277	(0.023)	[0.232, 0.323]
At1	0.276	(0.025)	[0.228, 0.324]
Gt1	0.276	(0.027)	[0.222, 0.329]
Ec1	0.274	(0.027)	[0.221, 0.328]
Nk1	0.274	(0.026)	[0.223, 0.325]
Bh1	0.273	(0.025)	[0.224, 0.322]
Ch1	0.272	(0.025)	[0.223, 0.321]
Ht1	0.272	(0.027)	[0.219, 0.324]
Jx1	0.272	(0.029)	[0.214, 0.329]
Lk1	0.272	(0.025)	[0.223, 0.322]
Ml1	0.270	(0.028)	[0.216, 0.324]
Jkx1	0.265	(0.025)	[0.215, 0.314]
Ex1	0.263	(0.029)	[0.207, 0.319]
Bv1	0.257	(0.029)	[0.201, 0.313]
Dc1	0.257	(0.026)	[0.207, 0.307]
Ik1	0.257	(0.030)	[0.197, 0.317]
Ecl1	0.254	(0.027)	[0.200, 0.307]
It1	0.253	(0.029)	[0.196, 0.310]
Dx1	0.251	(0.028)	[0.196, 0.306]
Ipx1	0.249	(0.025)	[0.200, 0.298]
Bx1	0.248	(0.030)	[0.190, 0.306]
Lv1	0.247	(0.029)	[0.190, 0.303]
Lp1	0.245	(0.027)	[0.192, 0.298]
Mhl1	0.244	(0.025)	[0.195, 0.293]
Cv1	0.243	(0.026)	[0.193, 0.294]
Ak1	0.240	(0.029)	[0.182, 0.297]
Ic1	0.240	(0.028)	[0.186, 0.295]
Bsv1	0.239	(0.027)	[0.185, 0.293]
Akt1	0.237	(0.029)	[0.180, 0.293]
Fsv1	0.236	(0.027)	[0.184, 0.288]
El1	0.231	(0.031)	[0.169, 0.292]
Ix1	0.231	(0.027)	[0.179, 0.283]
Dh1	0.230	(0.027)	[0.176, 0.284]
Jk1	0.220	(0.027)	[0.168, 0.273]
Mh1	0.212	(0.028)	[0.156, 0.267]
Ipv1	0.211	(0.035)	[0.143, 0.279]
Gx1	0.205	(0.030)	[0.147, 0.263]
Ist1	0.199	(0.031)	[0.139, 0.259]
Iks1	0.191	(0.029)	[0.136, 0.247]
Nkp1	0.189	(0.032)	[0.126, 0.251]
Ecx1	0.185	(0.031)	[0.125, 0.246]
Hst1	0.185	(0.029)	[0.127, 0.242]
Icp1	0.184	(0.028)	[0.129, 0.239]
Bhx1	0.175	(0.031)	[0.114, 0.236]
Ip1	0.175	(0.027)	[0.121, 0.228]
Lkl1	0.174	(0.032)	[0.111, 0.237]
Dcx1	0.173	(0.029)	[0.117, 0.229]
Lpv1	0.157	(0.034)	[0.091, 0.224]
Chv1	0.153	(0.030)	[0.095, 0.211]
Mc1	0.145	(0.033)	[0.080, 0.210]
Llp1	0.132	(0.032)	[0.069, 0.195]
Mch1	0.071	(0.037)	[-0.002, 0.143]
Dch1	0.062	(0.039)	[-0.015, 0.138]
Kkl1	-0.033	(0.038)	[-0.108, 0.041]

The complete item set has a homogeneity value H (se, 95%CI) of 0.230, (0.010), [0.210, 0.249]: this is significantly lower than the recommended 0.30, suggesting that the scale is not homogeneous. This is further supported by the fact that few items have a homogeneity around or above this value (only 5 if we consider the point estimate, and 33 out of 66 if we consider a 95%CI with an upper limit above 0.3). Interestingly, the homogeneity of related items (different presentations and different orders of the tones) are overall very similar, suggesting again that this an intrinsic property of the segments and tone(s) and not of their repeated presentation of order of tones.

Table S18. ‘Flipped’ weird items: MSA: aisp for increasing H thresholds (c) for all items.

Items	c=0.05	c=0.10	c=0.15	c=0.20	c=0.25	c=0.30	c=0.35	c=0.40	c=0.45	c=0.50	c=0.55	c=0.60
Ak1	1	1	1	1	1	3	13	0	0	0	0	NA
Akt1	1	1	1	1	2	2	2	13	0	0	0	NA
At1	1	1	1	1	1	1	11	0	0	0	0	NA
Bh1	1	1	1	1	1	1	4	3	0	0	0	NA
Bhx1	1	1	1	1	1	7	0	0	0	0	0	NA
Bs1	1	1	1	1	1	1	1	5	6	0	0	NA
Bsv1	1	1	1	1	2	2	12	0	0	0	0	NA
Bv1	1	1	1	1	1	1	3	5	6	6	0	NA
Bx1	1	1	1	1	1	1	8	10	0	0	0	NA
Ch1	1	1	1	1	1	1	3	11	0	0	0	NA
Chv1	1	1	1	0	0	0	0	0	0	0	0	NA
Cv1	1	1	1	1	1	7	11	11	0	0	0	NA
Dc1	1	1	1	1	1	1	5	0	0	0	0	NA
Dch1	2	2	2	2	0	4	10	0	0	0	0	NA
Dcx1	2	2	2	2	2	4	10	0	0	0	0	NA
Dh1	1	1	1	1	1	5	0	0	12	0	0	NA
Dx1	1	1	1	1	1	1	9	6	7	7	0	NA
Ec1	1	1	1	1	1	1	6	10	0	0	0	NA
Ecl1	1	1	1	1	1	2	2	13	0	0	0	NA
Ecx1	2	2	2	2	0	0	12	0	9	0	0	NA
El1	1	1	1	1	1	5	9	0	0	0	0	NA
Elx1	1	1	1	1	1	1	1	1	1	1	1	NA
Ex1	1	1	1	1	1	1	4	4	5	5	0	NA
Fs1	1	1	1	1	1	1	1	6	7	7	0	NA
Fsv1	1	1	1	1	2	2	2	0	0	0	0	NA
Fv1	1	1	1	1	1	1	1	12	3	3	3	NA
Gt1	1	1	1	1	1	1	3	5	12	0	0	NA
Gtx1	1	1	1	1	1	1	1	1	1	1	1	NA
Gx1	1	1	1	1	1	0	0	0	0	0	0	NA
Hs1	1	1	1	1	1	1	1	4	5	5	0	NA
Hst1	3	3	3	4	0	0	0	0	0	0	0	NA
Ht1	1	1	1	1	1	1	5	7	8	0	0	NA
Ic1	1	1	1	1	1	3	13	0	0	0	0	NA
Icp1	1	1	1	2	2	0	0	0	0	0	0	NA
Ik1	1	1	1	1	1	1	0	0	0	0	0	NA
Iks1	1	1	1	1	2	2	0	0	0	0	0	NA
Ip1	1	1	1	3	0	0	0	0	0	0	0	NA
Ipv1	1	1	1	1	2	2	2	2	2	2	2	NA
Ipx1	1	1	1	1	2	2	2	2	2	2	2	NA
Is1	1	1	1	1	1	1	1	12	3	0	0	NA
Ist1	1	1	1	1	3	3	0	0	0	0	0	NA
It1	1	1	1	1	1	3	6	8	10	0	0	NA
Iv1	1	1	1	1	1	1	1	3	4	4	4	NA
Ix1	1	1	1	1	1	0	8	10	0	0	0	NA
Jk1	1	1	1	1	3	3	6	8	10	0	0	NA
Jkx1	1	1	1	1	1	1	1	1	9	0	0	NA
Jx1	1	1	1	1	1	1	1	3	4	4	4	NA
Kk1	1	1	1	1	1	1	1	3	0	0	0	NA
Kkl1	3	3	4	0	0	0	0	0	0	0	0	NA
Kl1	1	1	1	1	1	1	1	1	3	3	3	NA
Lk1	1	1	1	1	1	1	3	0	0	0	0	NA
Lkl1	1	1	1	2	2	6	7	9	11	0	0	NA
Ll1	1	1	1	1	1	1	5	7	8	0	0	NA
Llp1	3	3	3	4	0	0	0	0	0	0	0	NA
Lp1	1	1	1	1	1	0	4	4	0	0	0	NA
Lpv1	2	2	2	2	2	6	0	0	0	0	0	NA
Lv1	1	1	1	1	1	1	3	5	6	6	0	NA
Mc1	3	3	4	3	0	0	0	0	0	0	0	NA
Mch1	2	2	0	0	0	0	0	0	0	0	0	NA
Mcl1	1	1	1	1	1	2	2	2	0	0	0	NA
Mh1	1	1	1	1	1	0	0	0	0	0	0	NA
Mhl1	1	1	1	1	2	2	7	9	11	0	0	NA
Ml1	1	1	1	1	1	1	1	3	0	0	0	NA
Nk1	1	1	1	1	1	1	1	6	0	0	0	NA
Nkp1	1	1	1	1	2	2	0	0	0	0	0	NA
Np1	1	1	1	1	1	1	1	6	7	0	0	NA

Let’s iteratively remove the unscalable items for c ≤ 0.30:

After removing 21 items (Mch1, Chv1, Kkl1, Mc1, Ip1, Dch1, Ecx1, Hst1, Llp1, Dcx1, Lpv1, Gx1, Jk1, Icp1, Lkl1, Ix1, Lp1, Mh1, Ist1, It1, Iks1), we are left with the 45 items (Ak1, Akt1, At1, Bh1, Bhx1, Bs1, Bsv1, Bv1, Bx1, Ch1, Cv1, Dc1, Dh1, Dx1, Ec1, Ecl1, El1, Elx1, Ex1, Fs1, Fsv1, Fv1, Gt1, Gtx1, Hs1, Ht1, Ic1, Ik1, Ipv1, Ipx1, Is1, Iv1, Jkx1, Jx1, Kk1, Kl1, Lk1, Ll1, Lv1, Mcl1, Mhl1, Ml1, Nk1, Nkp1, Np1), covering both ‘same’ and ‘different’ items, which seem to form a single scale (more or less, especially at c = 0.30):

Table S19. MSA: aisp for increasing H thresholds (c) after retaning only 1 item per class and after removing the unscalable itmes.

Items	c=0.05	c=0.10	c=0.15	c=0.20	c=0.25	c=0.30
Ak1	1	1	1	1	1	3
Akt1	1	1	1	1	1	2
At1	1	1	1	1	1	1
Bh1	1	1	1	1	1	1
Bhx1	1	1	1	1	1	5
Bs1	1	1	1	1	1	1
Bsv1	1	1	1	1	2	2
Bv1	1	1	1	1	1	1
Bx1	1	1	1	1	1	1
Ch1	1	1	1	1	1	1
Cv1	1	1	1	1	1	5
Dc1	1	1	1	1	1	1
Dh1	1	1	1	1	1	4
Dx1	1	1	1	1	1	1
Ec1	1	1	1	1	1	1
Ecl1	1	1	1	1	1	2
El1	1	1	1	1	1	4
Elx1	1	1	1	1	1	1
Ex1	1	1	1	1	1	1
Fs1	1	1	1	1	1	1
Fsv1	1	1	1	1	1	2
Fv1	1	1	1	1	1	1
Gt1	1	1	1	1	1	1
Gtx1	1	1	1	1	1	1
Hs1	1	1	1	1	1	1
Ht1	1	1	1	1	1	1
Ic1	1	1	1	1	1	3
Ik1	1	1	1	1	1	1
Ipv1	1	1	1	1	1	2
Ipx1	1	1	1	1	1	2
Is1	1	1	1	1	1	1
Iv1	1	1	1	1	1	1
Jkx1	1	1	1	1	1	1
Jx1	1	1	1	1	1	1
Kk1	1	1	1	1	1	1
Kl1	1	1	1	1	1	1
Lk1	1	1	1	1	1	1
Ll1	1	1	1	1	1	1
Lv1	1	1	1	1	1	1
Mcl1	1	1	1	1	1	2
Mhl1	1	1	1	1	1	2
Ml1	1	1	1	1	1	1
Nk1	1	1	1	1	1	1
Nkp1	1	1	1	1	2	2
Np1	1	1	1	1	1	1

and the subscale’s H is now a much better 0.294, (0.013), [0.269, 0.320]:

Homogeneity for the kept subscale.

	Item H	se	95% ci
Ak1	0.265	(0.033)	[0.200, 0.330]
Akt1	0.263	(0.037)	[0.191, 0.335]
At1	0.308	(0.028)	[0.252, 0.363]
Bh1	0.310	(0.031)	[0.250, 0.371]
Bhx1	0.235	(0.040)	[0.156, 0.313]
Bs1	0.344	(0.027)	[0.292, 0.397]
Bsv1	0.245	(0.033)	[0.180, 0.311]
Bv1	0.306	(0.036)	[0.236, 0.376]
Bx1	0.292	(0.037)	[0.219, 0.364]
Ch1	0.299	(0.030)	[0.240, 0.358]
Cv1	0.266	(0.030)	[0.207, 0.324]
Dc1	0.292	(0.029)	[0.235, 0.349]
Dh1	0.265	(0.032)	[0.202, 0.329]
Dx1	0.290	(0.033)	[0.225, 0.355]
Ec1	0.300	(0.031)	[0.240, 0.360]
Ecl1	0.281	(0.033)	[0.216, 0.345]
El1	0.268	(0.035)	[0.200, 0.336]
Elx1	0.313	(0.034)	[0.246, 0.380]
Ex1	0.308	(0.036)	[0.237, 0.379]
Fs1	0.364	(0.027)	[0.311, 0.416]
Fsv1	0.257	(0.032)	[0.195, 0.320]
Fv1	0.334	(0.030)	[0.275, 0.393]
Gt1	0.305	(0.032)	[0.242, 0.368]
Gtx1	0.307	(0.025)	[0.258, 0.355]
Hs1	0.320	(0.032)	[0.257, 0.384]
Ht1	0.299	(0.030)	[0.240, 0.358]
Ic1	0.268	(0.032)	[0.205, 0.331]
Ik1	0.283	(0.034)	[0.217, 0.348]
Ipv1	0.255	(0.047)	[0.163, 0.346]
Ipx1	0.270	(0.029)	[0.213, 0.326]
Is1	0.326	(0.028)	[0.270, 0.381]
Iv1	0.340	(0.027)	[0.287, 0.394]
Jkx1	0.283	(0.030)	[0.224, 0.343]
Jx1	0.320	(0.036)	[0.249, 0.391]
Kk1	0.316	(0.030)	[0.257, 0.376]
Kl1	0.362	(0.037)	[0.290, 0.434]
Lk1	0.306	(0.029)	[0.249, 0.363]
Ll1	0.322	(0.034)	[0.256, 0.388]
Lv1	0.278	(0.033)	[0.213, 0.342]
Mcl1	0.301	(0.027)	[0.247, 0.355]
Mhl1	0.260	(0.031)	[0.200, 0.320]
Ml1	0.308	(0.034)	[0.241, 0.375]
Nk1	0.305	(0.030)	[0.247, 0.364]
Nkp1	0.232	(0.042)	[0.149, 0.315]
Np1	0.321	(0.031)	[0.261, 0.381]

22 items do not meet the local independence criterion (Ak1, Akt1, Bhx1, Bsv1, Ch1, Cv1, Dh1, Ec1, Ecl1, Elx1, Fs1, Fsv1, Gt1, Gtx1, Ipv1, Ipx1, Iv1, Jkx1, Lk1, Mcl1, Mhl1, Nkp1) and are excluded from the analysis, leaving the 23 items At1, Bh1, Bs1, Bv1, Bx1, Dc1, Dx1, El1, Ex1, Fv1, Hs1, Ht1, Ic1, Ik1, Is1, Jx1, Kk1, Kl1, Ll1, Lv1, Ml1, Nk1, Np1.

Monotonicity tests for the remaining items are shown below for default minsize:

Table S20. Monotonicity.

	ItemH	#ac	#vi	#vi/#ac	maxvi	sum	sum/#ac	zmax	#zsig	crit
At1	0.32	3	0	0	0	0	0	0	0	0
Bh1	0.35	3	0	0	0	0	0	0	0	0
Bs1	0.37	3	0	0	0	0	0	0	0	0
Bv1	0.34	3	0	0	0	0	0	0	0	0
Bx1	0.34	3	0	0	0	0	0	0	0	0
Dc1	0.33	3	0	0	0	0	0	0	0	0
Dx1	0.33	3	0	0	0	0	0	0	0	0
El1	0.29	3	0	0	0	0	0	0	0	0
Ex1	0.33	3	0	0	0	0	0	0	0	0
Fv1	0.37	3	0	0	0	0	0	0	0	0
Hs1	0.35	3	0	0	0	0	0	0	0	0
Ht1	0.34	3	0	0	0	0	0	0	0	0
Ic1	0.29	3	0	0	0	0	0	0	0	0
Ik1	0.31	3	0	0	0	0	0	0	0	0
Is1	0.33	3	0	0	0	0	0	0	0	0
Jx1	0.35	3	0	0	0	0	0	0	0	0
Kk1	0.35	3	0	0	0	0	0	0	0	0
Kl1	0.41	3	0	0	0	0	0	0	0	0
Ll1	0.34	3	0	0	0	0	0	0	0	0
Lv1	0.31	3	0	0	0	0	0	0	0	0
Ml1	0.34	3	0	0	0	0	0	0	0	0
Nk1	0.33	3	0	0	0	0	0	0	0	0
Np1	0.35	3	0	0	0	0	0	0	0	0

Invariant item ordering (IIO) tests are shown below for default minsize:

Table S21. Invariant item ordering (IIO).

	ItemH	#ac	#vi	#vi/#ac	maxvi	sum	sum/#ac	zmax	#zsig	crit
At1	0.32	44	1	0.02	0.04	0.04	0.0009	0.46	0	11
Bh1	0.35	44	1	0.02	0.04	0.04	0.0009	0.46	0	9
Bs1	0.37	44	2	0.05	0.09	0.12	0.0028	1.28	0	23
Bv1	0.34	44	0	0.00	0.00	0.00	0.0000	0.00	0	0
Bx1	0.34	44	0	0.00	0.00	0.00	0.0000	0.00	0	0
Dc1	0.33	44	0	0.00	0.00	0.00	0.0000	0.00	0	0
Dx1	0.33	44	5	0.11	0.05	0.22	0.0049	0.72	0	30
El1	0.29	44	1	0.02	0.06	0.06	0.0013	0.81	0	17
Ex1	0.33	44	0	0.00	0.00	0.00	0.0000	0.00	0	0
Fv1	0.37	44	1	0.02	0.05	0.05	0.0011	0.62	0	10
Hs1	0.35	44	0	0.00	0.00	0.00	0.0000	0.00	0	0
Ht1	0.34	44	1	0.02	0.06	0.06	0.0013	0.81	0	14
Ic1	0.29	44	4	0.09	0.09	0.28	0.0064	2.11	1	55
Ik1	0.31	44	0	0.00	0.00	0.00	0.0000	0.00	0	0
Is1	0.33	44	2	0.05	0.09	0.14	0.0032	1.36	0	27
Jx1	0.35	44	0	0.00	0.00	0.00	0.0000	0.00	0	0
Kk1	0.35	44	0	0.00	0.00	0.00	0.0000	0.00	0	0
Kl1	0.41	44	0	0.00	0.00	0.00	0.0000	0.00	0	0
Ll1	0.34	44	0	0.00	0.00	0.00	0.0000	0.00	0	0
Lv1	0.31	44	0	0.00	0.00	0.00	0.0000	0.00	0	0
Ml1	0.34	44	0	0.00	0.00	0.00	0.0000	0.00	0	0
Nk1	0.33	44	0	0.00	0.00	0.00	0.0000	0.00	0	0
Np1	0.35	44	2	0.05	0.06	0.11	0.0025	2.11	1	38

and the subscale’s H is now a much better 0.337, (0.024), [0.290, 0.384]:

Homogeneity for the kept subscale.

	Item H	se	95% ci
At1	0.315	(0.036)	[0.245, 0.386]
Bh1	0.349	(0.038)	[0.275, 0.423]
Bs1	0.367	(0.034)	[0.300, 0.434]
Bv1	0.340	(0.041)	[0.260, 0.419]
Bx1	0.336	(0.044)	[0.250, 0.423]
Dc1	0.326	(0.039)	[0.250, 0.403]
Dx1	0.327	(0.039)	[0.251, 0.404]
El1	0.292	(0.041)	[0.212, 0.373]
Ex1	0.332	(0.042)	[0.249, 0.414]
Fv1	0.366	(0.036)	[0.296, 0.437]
Hs1	0.349	(0.042)	[0.267, 0.431]
Ht1	0.339	(0.037)	[0.267, 0.412]
Ic1	0.287	(0.039)	[0.211, 0.363]
Ik1	0.309	(0.040)	[0.232, 0.387]
Is1	0.333	(0.034)	[0.266, 0.400]
Jx1	0.351	(0.041)	[0.270, 0.432]
Kk1	0.354	(0.038)	[0.280, 0.427]
Kl1	0.412	(0.046)	[0.321, 0.503]
Ll1	0.341	(0.039)	[0.265, 0.417]
Lv1	0.308	(0.040)	[0.230, 0.386]
Ml1	0.344	(0.039)	[0.267, 0.421]
Nk1	0.333	(0.037)	[0.261, 0.405]
Np1	0.349	(0.037)	[0.276, 0.421]

However, it can be seen that this subscale is composed entirely of ‘same’ items, which suggests that IRT/Mokken are not appropriate for analyzing such data, but they were, nevertheless, extremely useful in helping to detect the “weird” items.

2.4.6 Rewind and restart: dealing with the “weird” items

Given the above analyses, it seems that the optimal way forward is:

1. the “weird” items (Bhx, Hst, Ist, Kkl1 and Llp, in all presentations and variants) require some sort of “special treatment”, prompting us to perform three independent analyses using identical methods, as follows:
   • keeping these items as they are (denoted as “original”),
   • removing these items (denoted as “removed”), and
   • recoding them as ‘same’ items (denoted as “recoded”).
2. summarizing the participants’ performance on this task as:
   • the % of correct responses across all items and presentations (this is a very simple, natural and frequently used approach),
   • as suggested by the PCA and EFA analyses, we estimate the % of correct responses separately for the ‘same’ and ‘different’ items, and
   • using Signal Detection Theory (Macmillan & Creelman, 1991), we estimate the sensitivity and the bias.

2.4.6.1 The three datasets

These three tone datasets all have the same number of participants (492, as we did not yet remove any outliers), but different number of items: 208 (original), 188 (removed), and 208 (recoded) respectively.

As a reminder, there are 20 “weird” items in total (Bhx1, Bhx2, Bxh1, Bxh2, Hst1, Hst2, Hts1, Hts2, Ist1, Ist2, Its1, Its2, Kkl1, Kkl2, Klk1, Klk2, Llp1, Llp2, Lpl1, Lpl2).

2.4.6.2 The three performance estimates

2.4.6.2.1 % of correct responses across all items and presentations

Figure S42. Histogram of the % correct on each of the three datasets. Figure generated using R version 4.3.3 (2024-02-29)

	pc_tot_orig	pc_tot_remv	pc_tot_recd
pc_tot_orig	1	0.97	0.9
pc_tot_remv	0.97	1	0.97
pc_tot_recd	0.9	0.97	1

Figure S43. Heatmap with clustering for the Spearman correlations between % correct total across the three datasets. Figure generated using R version 4.3.3 (2024-02-29)

The correlations between the total % correct across the three datasets are very large (≥0.90) suggesting that we can simply use any one of them.

2.4.6.2.2 % of correct responses for ‘same’ and ‘different’ itmes

Figure S44. Histogram of the % correct ‘same’ (top) and ‘different’ (bottom) on each of the three datasets. Figure generated using R version 4.3.3 (2024-02-29)

Table continues below

	pc_same_orig	pc_diff_orig	pc_same_remv	pc_diff_remv
pc_same_orig	1	0.23	1	0.34
pc_diff_orig	0.23	1	0.23	0.97
pc_same_remv	1	0.23	1	0.34
pc_diff_remv	0.34	0.97	0.34	1
pc_same_recd	0.94	0.08	0.94	0.24
pc_diff_recd	0.34	0.97	0.34	1

	pc_same_recd	pc_diff_recd
pc_same_orig	0.94	0.34
pc_diff_orig	0.08	0.97
pc_same_remv	0.94	0.34
pc_diff_remv	0.24	1
pc_same_recd	1	0.24
pc_diff_recd	0.24	1

Figure S45. Heatmap with clustering for the Spearman correlations between % correct for ‘same’ and ‘different’ itmes across the three datasets. Figure generated using R version 4.3.3 (2024-02-29)

It is a similar story here, with the % correct for the ‘same’ items being highly intercorrelated (≥0.94) as are those for the ‘different’ items (≥0.97), but the correlations between these two types are low (between 0.09 and 0.35) with the lowest for the ‘original’ dataset; therefore we will use the results for the ‘remove’ (or the ‘recode’) dataset.

2.4.6.2.3 Signal Detection Theory (sensitivity and bias)

We estimate the following measures (using psycho::dprime(), see Pallier (2002), https://bookdown.org/danbarch/psy_207_advanced_stats_I/signal-detection-theory.html and https://www.birmingham.ac.uk/Documents/college-les/psych/vision-laboratory/sdtintro.pdf for details):

• d’ and beta (or c): the “classic” measures of sensitivity (d’) and bias (beta):
  • d’ is theoretically positive and varies between 0.0 (no sensitivity) to +∞ (in practice, up to ~3), with higher values representing higher sensitivity (please note that it is possible to obtain small negative estimates when there is no capacity to detect the signal but this should be treated as essentially 0.0)
  • beta varies between 0.0 (bias towards ‘different’) to +∞ (bias towards ‘same’), where 1.0 represents an unbiased observer,
  • c is an alternative estimator of the bias representing the number of standard deviations from the midpoint between the ‘signal, and the ’signal + noise’ distributions, and is a symmetric continuum from negative values (preferring ‘different’ responses), to 0 (“unbiased”) and to positive values (preferring ‘same’ responses);
• A’ and B’’D: are the non-parametric counterparts of d’ and beta:
  • A’ near 0.5 represents chance, while closer to 1.0 represents good detection,
  • B’‘D varies between -1 (bias towards ’different’) and 1 (bias towards ‘same’), with 0 being an unbiased observer.

Figure S46. Histogram of the % correct ‘same’ (top) and ‘different’ (bottom) on each of the three datasets. Figure generated using R version 4.3.3 (2024-02-29)

Table continues below

	dprime_orig	beta_orig	c_orig	aprime_orig	bppd_orig
dprime_orig	1	0.8	0.55	0.92	0.8
beta_orig	0.8	1	0.9	0.54	1
c_orig	0.55	0.9	1	0.29	0.91
aprime_orig	0.92	0.54	0.29	1	0.55
bppd_orig	0.8	1	0.91	0.55	1
dprime_remv	0.98	0.71	0.46	0.95	0.71
beta_remv	0.51	0.89	0.91	0.21	0.89
c_remv	0.28	0.74	0.92	-0.01	0.75
aprime_remv	0.91	0.55	0.3	0.98	0.56
bppd_remv	0.53	0.89	0.92	0.23	0.9
dprime_recd	0.93	0.69	0.46	0.9	0.69
beta_recd	0.26	0.74	0.84	-0.06	0.73
c_recd	0.07	0.59	0.82	-0.23	0.6
aprime_recd	0.9	0.59	0.35	0.95	0.6
bppd_recd	0.25	0.74	0.85	-0.07	0.73

Table continues below

	dprime_remv	beta_remv	c_remv	aprime_remv	bppd_remv
dprime_orig	0.98	0.51	0.28	0.91	0.53
beta_orig	0.71	0.89	0.74	0.55	0.89
c_orig	0.46	0.91	0.92	0.3	0.92
aprime_orig	0.95	0.21	-0.01	0.98	0.23
bppd_orig	0.71	0.89	0.75	0.56	0.9
dprime_remv	1	0.38	0.14	0.96	0.39
beta_remv	0.38	1	0.92	0.21	0.99
c_remv	0.14	0.92	1	-0.02	0.92
aprime_remv	0.96	0.21	-0.02	1	0.23
bppd_remv	0.39	0.99	0.92	0.23	1
dprime_recd	0.97	0.36	0.13	0.94	0.37
beta_recd	0.14	0.92	0.91	-0.03	0.9
c_recd	-0.05	0.81	0.95	-0.21	0.81
aprime_recd	0.96	0.25	0.03	0.99	0.27
bppd_recd	0.13	0.91	0.92	-0.04	0.9

	dprime_recd	beta_recd	c_recd	aprime_recd	bppd_recd
dprime_orig	0.93	0.26	0.07	0.9	0.25
beta_orig	0.69	0.74	0.59	0.59	0.74
c_orig	0.46	0.84	0.82	0.35	0.85
aprime_orig	0.9	-0.06	-0.23	0.95	-0.07
bppd_orig	0.69	0.73	0.6	0.6	0.73
dprime_remv	0.97	0.14	-0.05	0.96	0.13
beta_remv	0.36	0.92	0.81	0.25	0.91
c_remv	0.13	0.91	0.95	0.03	0.92
aprime_remv	0.94	-0.03	-0.21	0.99	-0.04
bppd_remv	0.37	0.9	0.81	0.27	0.9
dprime_recd	1	0.18	-0.01	0.97	0.17
beta_recd	0.18	1	0.93	0.05	1
c_recd	-0.01	0.93	1	-0.14	0.94
aprime_recd	0.97	0.05	-0.14	1	0.04
bppd_recd	0.17	1	0.94	0.04	1

Figure S47. Heatmap with clustering for the Spearman correlations between SDT sensitivity and bias measures across the three datasets. Figure generated using R version 4.3.3 (2024-02-29)

First, the signal estimates d’ and A’ are very highly intercorrelated across datasets (≥0.93 for d’ and ≥0.95 for A’) and between them (between 0.91 and 0.96). Second, within each dataset, beta and c have high correlations of ~0.9. Between datasets, beta for ‘remove’ seems to have the best intercorrelations (0.92 with ‘recode’ and 0.89 with ‘original’) while the correlation for ‘original’ and ‘recode’ is 0.75; however, c shows much higher intercorrelations (between 0.82 and 0.95). B’‘D for ’original’ is moderately correlated with the other two (0.73 with ‘recode’ and 0.90 with ‘remove’), while these two correlate at 0.90. Within datasets, B’’D correlates very strongly with c (around 0.92) and almost perfectly with beta (≥0.99).

Coupled with the more natural interpretation of some of these estimates estimates, we will focus primarily on d’ and c, but also keep A’ and B’’D in mind.

2.4.6.2.4 Combining the estimates

From the above, it seems that either the ‘remove’ or the ‘recode’ datasets would be best if we had to use a single dataset, and there seems to be a slight data-driven advantage for the former (in the sense that it has the best correlations with the other two datasets), but at the cost of completely losing the information provided by the “weird” items – given these, we will continue using the three datasets for now. In terms of actual estimates, we will keep the % correct responses overall and for the ‘same’ and ‘different’ items separately, while for the Signal Detection Theory we will focus on d’ and c.

However, first we will detect those participants with very high biases (one way or another):

Figure S48. Histogram of the bias c on each of the three datasets (repeated from above). Figure generated using R version 4.3.3 (2024-02-29)

It can be seen that in all three datasets there is an overall bias towards answering ‘same’ (c > 0, highly significant in all three cases), but much smaller (and statistically highly significantly so) for the ‘remove’ (0.3) and especially ‘recode’ (0.2) than for the ‘original’ (0.52) dataset.

Figure S49. Histogram of the bias c on each of the three datasets. Figure generated using R version 4.3.3 (2024-02-29)

It is clear that there are some participants with strong biases in all three datasets; let’s identify them. First, there is one participant which systematically answered “same” irrespective of the item:

Table S22. One participant systematcally answered ‘same’

age	gender	music_years	education_years	location
51	F	0	2	B

Second, there are no other participants with strong biases (≤-2 or ≥2) in any dataset and only a few with c between -1 and 1, so let’ remove this one only.

Interestingly, while for the “original” dataset there are quite a few participants with biases |c|≥1 (2 with c≤-1 and 34 with c≥1), there are fewer for the “remove” (3 with c≤-1 and 14 with c≥1) and for the “recode” (3 with c≤-1 and 11 with c≥1) datasets, again supporting the idea that the “weird” items are treated as ‘same’ and not ‘different’.

Focusing on the “de-biased” signal sensitivity d’, it interesting to note that it is higher, on average, on the “recoded” (2.3) dataset than on the “removed” (2.49) dataset and than on the “original” (2.04) dataset (all differences are highly significant as judged with two-sample t-tests), again supporting the view that the “weird” items are ‘same’ and not ‘different’.

Let’s look at the relationships between estimates within datasets:

Figure S50. Heatmap with clustering for the Spearman correlations between various measures in each dataset. Figure generated using R version 4.3.3 (2024-02-29)

Figure S51. The three main measures of success on each dataset. Figure generated using R version 4.3.3 (2024-02-29)

d’ and A’ are highly correlated (≥ 0.92), as expected, and each is highly correlated with the % total correct responses: d’ (between 0.87 and 0.96) and A’ is virtually perfectly correlated (≥0.97).

2.4.6.2.5 Consider the “weird” items separately

Keeping, removing or recoding the “weird” items all share the downside that they ignore the participants that seemingly answered correctly for these items. Therefore, we will consider these items as ‘different’ items, together with their associated ‘same’ items, separately.

Figure S52. Histograms of the measures on the ‘weird’ dataset. Figure generated using R version 4.3.3 (2024-02-29)

A expected (see Table below), there is a low overall % correct responses due to the very low % correct responses on the ‘different’ items, which is reflected in rather low sensitivities, with strong biases towards ‘same’ responses compared with the other three datasets:

Table S23. Comparing the means of the measures across all four datasets.

dataset	% total	% same	% different	d’	beta	c	A’	B’’D
original	78.6	89.0	68.1	2.04	5.69	0.52	0.85	0.56
remove	84.5	89.0	79.0	2.49	3.71	0.29	0.88	0.37
recode	83.8	87.1	79.0	2.30	2.54	0.20	0.88	0.22
weird	60.7	90.0	22.6	0.71	3.09	1.16	0.69	0.60

Importantly, the maximum % correct is 94.2% overall, 100% for ‘same’ and 97.1% for ‘different’, suggesting that same participants seem to have answered correctly. Let’s see who these participants are and how their responses on the ‘non-weird’ items are like:

Figure S53. The measures of success for the ‘weird’ and ‘non-weird’ (‘remove’) items. Figure generated using R version 4.3.3 (2024-02-29)

And let’s look at their correlations:

Figure S54. Heatmap with clustering for the correlations between all measures for the ‘weird’ and ‘non-weird’ (‘remove’ dataset) items. Figure generated using R version 4.3.3 (2024-02-29)

The important relationships are those between the two sets (“weird” vs “non-weird” items) for:

% total correct responses: their correlation is 0.50 overall, but this is likely heavily biased by the ‘same’ items (which, as expected, behaves the same for the “weird” and “non-weird” items),

% correct for the ‘different’ items: while overall there is no correlation (-0.03, p>0.05), there seem to be two or three groups of participants (cluster::clusGap() suggests 3 clusters, while NbClust::NbClust() finds that 8 methods suggest 2 clusters and 7 suggest 3):

Figure S55. K-means clustering of the participants using the % correct for ‘different’ on the ‘weird’ and ‘non-weird’ items, with k=2 (left) and k=3 (right), trying to keep the colors and symbols of the corresponding clusters similar. Please note the order of the ‘numbers’ (i.e., ‘1’, ‘2’ and ‘3’) in the legend is arbitrary. Figure generated using R version 4.3.3 (2024-02-29)

It can be seen that the 3-clusters solution is more or less splitting one of the clusters of the 2-clusters solution, with the rough correspondences between the k=2 and k=3 clusters 1(2) ≈ 1(3) [red in the figure] and 2(2) ≈ 2(3)+3(3) [shades of blue in the figure], where the first digit is the cluster ‘number’ (which is arbitrary) and in parentheses the number of clusters, k, so 1(2) means cluster ‘1’ of the k=2 solution. Within each cluster, we have the following correlations between the “weird” and “non-weird” % correct:

Table S24. Correlations (Pearson and Spearman) between % correct ‘different’ for the ‘weird’ and ‘non-weird’ items on the full dataset (1 cluster) and for each cluster separately (the same cluster notations as in the figure above).

# clusters	cluster 1	cluster 2	cluster 3
full dataset	r=-0.18, p=9.34×10-5; ρ=-0.03, p=0.557	-	-
k=2	r=0.45, p=3.92×10-6; ρ=0.37, p=1.94×10-4	r=0.36, p=1.66×10-13; ρ=0.36, p=3.22×10-13	-
k=3	r=0.49, p=1.29×10-6; ρ=0.42, p=5.73×10-5	r=0.09, p=0.281; ρ=0.11, p=0.193	r=0.21, p=4.63×10-4; ρ=0.21, p=6.84×10-4

It can be seen that, while on the full dataset there basically is no correlation (Pearson’s seems affected by a few outliers), in clusters 1(2), 1(3), 2(2) and 3(3) there is a strong positive and highly significant correlation, while on cluster 2(3) there is no correlation.

For k=2, cluster 1(2) comprises only 95 or 19.4% participants which seem to respond at chance level to the “non-weird” ‘different’ items (between 7.1% and 81%, mean = 47.5% and median = 48.8%) and have low % for the “weird” items (between 0% and 70%, mean = 37% and median = 40%). Here, there is a significant positive relationship between the two % correct measures, the % correct of the “non-weird” items predicting very well that for the “weird” items (liner regression β=0.63±0.13, p=3.92×10^-6).

The same pattern holds for the corresponding k=3, cluster 1(3): this comprises 86 or 17.6% participants which seem to respond at chance level to the “non-weird” ‘different’ items (between 7.1% and 65.5%, mean = 45.6% and median = 47%) and have low % for the “weird” items (between 0% and 70%, mean = 37.2% and median = 40%). Here, there is a significant positive relationship between the two % correct measures, the % correct of the “non-weird” items predicting very well that for the “weird” items (liner regression β=0.73±0.14, p=1.29×10^-6).

For k=2, cluster 2(2) comprises the vast majority of the participants (394 or 80.6%) which have high % correct responses for the “non-weird” ‘different’ items (≥ 59.5%, mean = 86.6% and median = 88.1%) but mostly low % for the “weird” items (≤ 90%, mean = 19.1% and median = 15%). Here, there is a significant positive relationship between the two % correct measures, the % correct of the “non-weird” items predicting that for the “weird” items (liner regression β=0.60±0.08, p=1.66×10^-13).

This group roughly splits in two for k=3: cluster 2(3) that comprises 86 or 17.6% the participants with relatively high % for the “non-weird” items (≥ 7.1%, mean = 45.6% and median = 47%) and very low for the “weird” items (≤ 70%, mean = 37.2% and median = 40%), basically those that clearly treated the “weird” items as being of the ‘same’ type. There is no relationship between the two % correct measures (β=0.15±0.14, p=0.281). The other part forms cluster 3(3) that comprises 265 or 54.2% the participants with high % for the “non-weird” items (≥ 51.2%, mean = 84.3% and median = 85.7%) and low for the “weird” items (≤ 25%, mean = 11% and median = 10%). However, here there is no relationship between the two % correct measures, with a significant positive prediction of % correct of the “weird” items from the “non-weird” ones (β=0.15±0.04, p=4.63×10^-4).

Importantly, the % correct for the “weird” items (which are nominally ‘different’) have very strong and highly significant negative correlations with the % correct for the ‘same’ “non-weird” items, and their linear regressions have negative and slopes β of comparable size (-0.90 ≤ βs ≤ -0.50) on the whole dataset and in each cluster separately:

Table S25. Correlations (Pearson and Spearman) between % correct for the ‘weird’ and for the ‘same’ for the ‘non-weird’ items on the full dataset (1 cluster) and for each cluster separately (the same cluster notations as in the figure above).

# clusters	cluster 1	cluster 2	cluster 3
full dataset	r=-0.69, p=6.14×10-70; ρ=-0.64, p=2.78×10-58	-	-
k=2	r=-0.77, p=6.53×10-20; ρ=-0.61, p=3.42×10-11	r=-0.54, p=6.42×10-31; ρ=-0.51, p=4.68×10-27	-
k=3	r=-0.79, p=3.1×10-19; ρ=-0.61, p=6.67×10-10	r=-0.57, p=2.64×10-13; ρ=-0.28, p=7.08×10-4	r=-0.30, p=9.95×10-7; ρ=-0.29, p=1.77×10-6

Figure S56. The % correct responses for the ‘weird’ (y axis) vs the % correct responses foe the ‘same’ ‘non-weird’ (aka ‘remove’) items (x axis) overall (left), for k=2 (middle) and k=3 (right), keeping the same cluster colors and symbols as above. Figure generated using R version 4.3.3 (2024-02-29)

This is precisely the pattern to be expected if the “weird” items behave like the ‘same’ items: the participants with higher performance on the same items have lower performance on the “weird” items because their responses are in fact “flipped”.

2.4.6.3 Conclusions

It turns out that the tone task is far from simple, in particular, that the 5 items Bhx, Hst, Ist, Kkl1 and Llp (so-called “weird” items) clearly behave like the ‘same’ items and not as the other ‘different’ items. While this is interesting question from a phonological/phonetic point of view, we must leave this for future research.

Here, the relevant questions is “what to do with these items?”. There are three basic options: (1) leave them as they are (the “original” dataset), (2) simply drop them (the “removed” dataset), or (3) consider them as ‘same’ items and “flip” their responses (the “recode” dataset). From the analyses performed above and theoretical considerations, we will focus on the “recode” dataset but also keep the original dataset for reference.

With these we have the following primary and secondary measures:

• primary measures: these are estimates of the performance at discriminating the tones: % correct responses overall and d’ on the “original” and the “recode” datasets, and
• secondary measures:
  • the % correct responses overall and d’ on the “remove” dataset, and
  • % correct responses for ‘same’ and ‘different’ items separately, and the measure of bias c on all three datasets.

Finally, please note that while d’ can arguably be modeled well using linear regression, for the % correct responses we have several potential choices:

1. linear regression on the % of correct responses: this is arguably wrong as it assumes values outside the [0,100] range and we will not pursue it here except for complex path models where beta regression (see below) does not currently work and where the fitted models using both approaches are very similar.
2. beta regression on the proportion of correct responses (i.e., on the %/100.0): this is a technique implemented in R by, for example, glmmTMB(..., family=beta_family()), standardly used to model proportion data, but is has two drawbacks: it cannot deal with proportions of exactly 0.0 and 1.0, requiring these to be converted to something almost 0.0 and 1.0 respectively, and, for most participants, it produces relatively hard-to-interpret estimates.
3. logistic regression on the actual responses (or, equivalently, on the counts of “0” and “1” responses): this is probably the best to model our data as logistic regression is very well supported in R and most practitioners know how to interpret its results.

Therefore, we will systematically perform logistic regression on the counts of “0” (“incorrect”) and “1” (“correct”) responses, but we might still plot and show the % of correct responses when appropriate.

3 Results

3.1 Correlations between measures

Pearson’s:

Figure S57. Heatmap with clustering for the Pearson’s correlations between the conitnuous measures of interest. Figure generated using R version 4.3.3 (2024-02-29)

Spearman’s:

Figure S58. Heatmap with clustering for the Spearman’s correlations between the conitnuous measures of interest. Figure generated using R version 4.3.3 (2024-02-29)

It can be seen that:

• the results using either correlation estimate are very similar,
• age is negatively correlated (significantly or not) with all the other variables, especially strongly with years of education and the working memory task, and much less with the various measures of tone task performance,
• gender is correlated with years of education (higher for males),
• the location correlates with years of education (slightly higher in A) and with the performance on the tone task (especially the ‘different’ items, better for A),
• % correct answers and d’ on both datasets behave very similarly: negatively affected by age, by location (better in A) and for more education, gender (slightly better for males), and positively effected by working memory,
• % correct answers for ‘different’ items on both datasets are very similar and like % total and d’ above,
• % correct answers for the ‘same’ items on both datasets are very similar and like % total and d’ above,
• finally, the bias estimators c behave strikingly differently on both datasets: while on the ‘original’ it is negatively affected by age and positively by education and working memory, on the ‘recoded’ it does not seem influenced by any such covariate.

These bi-variate correlations, however, might hide more complex relationships between the measures of interest and covariates, that can be tested using multiple regression, mediation and (piecewise) path and structural models.

3.2 The working memory task

We use here the normalized working memory performance estimate (wm_norm).

3.2.1 Location and family

There are no significant differences between the two main locations (A and B) in terms of the working memory task performance (linear regression β_B-A=-0.0075, p=0.688), and, for those 91 participants with information about family relationships, the generation they belong to also has no effect (linear regression β_old-young=-0.081, p=0.122), and, moreover, there is no clustering within families (the linear model with family as a random effect has an ICC of 2.4%), suggesting that we need not model these factors here.

3.2.2 Multiple regression

A linear multiple linear regression of the working memory task performance on age, gender and years of education and all their interactions simplifies (using manual simplification based on F-test’s p value) to a model with main effects only:

Call:
lm(formula = wm_norm ~ age + gender + education_years, data = d)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.45016 -0.09722 -0.00813  0.09104  0.50216 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)      0.5539387  0.0365383  15.160   <2e-16 ***
age             -0.0058270  0.0006713  -8.680   <2e-16 ***
genderM         -0.0320631  0.0142637  -2.248    0.025 *  
education_years  0.0206672  0.0020358  10.152   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1425 on 485 degrees of freedom
Multiple R-squared:  0.509, Adjusted R-squared:  0.506 
F-statistic: 167.6 on 3 and 485 DF,  p-value: < 2.2e-16

Figure S59. Untransformed slopes with standard errors. Figure generated using R version 4.3.3 (2024-02-29)

suggesting that age has a negative effect, years of education a positive effect, and that males have worse performance than the females. However, it is possible that the causal model is more complex, with the effect of gender and age largely mediated by the years of education.

3.2.3 Mediation: gender → education → working memory

Indeed, fitting a mediation model where gender influences working memory through years of education (N.B. while the outcome model is a linear regression of working memory performance on the mediator and treatment, the mediator model is the Poisson regression of years of education on the treatment because the years of education is a count variable) finds a highly significant positive indirect effect (ACME), but also a significant direct effect (ADE); as fitted using mediation::mediate():

Causal Mediation Analysis 

Quasi-Bayesian Confidence Intervals

               Estimate 95% CI Lower 95% CI Upper p-value    
ACME            0.07474      0.04916         0.10  <2e-16 ***
ADE            -0.04001     -0.07098        -0.01   0.015 *  
Total Effect    0.03473     -0.00461         0.07   0.084 .  
Prop. Mediated  2.05904     -7.70715        13.87   0.084 .  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Sample Size Used: 489 


Simulations: 10000 

and as fitted using piecewiseSEM::psem():

Structural Equation Model of wm_task_results$med_gender__education$piecewise$model 

Call:
  education_years ~ gender_n
  wm_norm ~ gender_n + education_years

    AIC
 2833.359

---
Tests of directed separation:

 No independence claims present. Tests of directed separation not possible.

--
Global goodness-of-fit:

Chi-Squared = 0 with P-value = 1 and on 0 degrees of freedom
Fisher's C = NA with P-value = NA and on 0 degrees of freedom

---
Coefficients:

         Response       Predictor Estimate Std.Error  DF Crit.Value P.Value
  education_years        gender_n   0.3653    0.0373 487     9.7828  0.0000
          wm_norm        gender_n  -0.0403    0.0153 486    -2.6356  0.0087
          wm_norm education_years   0.0320    0.0017 486    19.1132  0.0000
  Std.Estimate    
        0.2563 ***
       -0.0932  **
        0.6755 ***

  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05

---
Individual R-squared:

         Response     method R.squared
  education_years nagelkerke      0.18
          wm_norm       none      0.43

Figure S60. Mediation model of gender, years of education and working memory performance showing the standardized coefficients (fitted using piecewiseSEM::psem()). Figure generated using R version 4.3.3 (2024-02-29)

Moreover, testing this partial mediation model against the full mediation model (that does not include a direct effect but only the indirect effect) using d-separation and model comparison finds that while the effect of gender is mostly mediated through years of education (males have more and years of education are positively related to working memory), the direct effect of gender on working memory also matters (d-sep p=0.009, model comparison χ²(1)=6.9, p=0.008).

3.2.4 Mediation: age → education → working memory

Likewise, fitting a mediation model where age influences working memory through years of education finds a highly significant negative direct effect (ADE), but also a significant negative mediated effect (ACME); as fitted using mediation::mediate():

Causal Mediation Analysis 

Quasi-Bayesian Confidence Intervals

               Estimate 95% CI Lower 95% CI Upper p-value    
ACME           -0.01300     -0.02510         0.00   0.024 *  
ADE            -0.00594     -0.00754         0.00  <2e-16 ***
Total Effect   -0.01894     -0.03072        -0.01   0.001 ***
Prop. Mediated  0.68258      0.22255         0.83   0.023 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Sample Size Used: 489 


Simulations: 10000 

and as fitted using piecewiseSEM::psem():

Structural Equation Model of wm_task_results$med_age__education$piecewise$model 

Call:
  education_years ~ age
  wm_norm ~ age + education_years

    AIC
 2289.804

---
Tests of directed separation:

 No independence claims present. Tests of directed separation not possible.

--
Global goodness-of-fit:

Chi-Squared = 0 with P-value = 1 and on 0 degrees of freedom
Fisher's C = NA with P-value = NA and on 0 degrees of freedom

---
Coefficients:

         Response       Predictor Estimate Std.Error  DF Crit.Value P.Value
  education_years             age  -0.0326    0.0013 487   -24.2750       0
          wm_norm             age  -0.0059    0.0007 486    -8.8119       0
          wm_norm education_years   0.0196    0.0020 486     9.8602       0
  Std.Estimate    
       -0.6394 ***
       -0.3691 ***
        0.4130 ***

  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05

---
Individual R-squared:

         Response     method R.squared
  education_years nagelkerke       0.7
          wm_norm       none       0.5

Figure S61. Mediation model of age, years of education and working memory performance showing the standardized coefficients (fitted using piecewiseSEM::psem()). Figure generated using R version 4.3.3 (2024-02-29)

Moreover, the effect of age is split between the direct negative effect and the mediated (negative effect on years of education) effect (d-sep p=2.19×10^-17, model comparison χ²(1)=72.5, p=0).

3.2.5 Conclusions

The working memory performance is not influenced by the location (as expected) and does not cluster within families, but is strongly positively correlated with years of education (causality is unclear but could be bidirectional), is strongly negatively influenced by age both directly and indirectly by negatively influencing the years of education, and by gender, mainly indirectly as the males have more years of education, but also directly, as the males seem to have slightly worse performance than the females.

3.3 The tone task

Given that the tone task results can be potentially analyses in several ways, we conducted a preliminary comparison to decide what would be the best approach here:

1. “flat” logistic regression (i.e., the participants and the items are “pooled” together): this has the advantage that is a very simple and well-understood regression model and works on the actual responses by a given individual to a given item, allowing thus, in principle, the modelling of item characteristics (e.g., ‘same’ vs ‘different’, ‘weird’ vs ‘non-weird’ or if it contains an actual word), but suffers from the same drawback as all models that ignore the hierarchical structure of the data namely that their estimation of significance may be extremely biased,
2. mixed-effects logistic regression including the participant and item as crossed random effects (aka ... + (1 | participant) + (1 | item)) which keeps the advantages of (a) but properly deal with its main shortcoming, and
3. beta regression on the % correct responses: this collapses the responses across items for a given participant addressing the issues of participant (but not of item) clustering.

We compared these on our data and we found that:

• all these methods produced comparable regression coefficient estimates, but
• method (a) is inappropriate as it results in overly optimistic p-values,
• the (adjusted) ICC of the participants and items is around 40%, confirming that we need to somehow address this clustering (and confirming the inadequacy of (a)),
• methods (b) and (c) agree not only in the point estimates but also in their standard errors and p-values
• method (b) is slower and may have convergence issues for complex fixed effects structures
• by using a single estimate per participant, (c) is similar both with the approach in Wong et al. (2020) and with the use of d’
• likewise, it is easier to introduce in mediation models using other variables of interest that have a single value per participant (e.g., gender, working memory, years of education…).

Therefore, we will perform the beta regression of the % correct responses, using a mixed-effects logistic regression as sanity check in some cases.

3.3.1 % correct total (recode)

We focus here on the % total correct responses on the ‘recoded’ dataset estimate (pcr = percent correct recoded). Given that these are percents bounded, by definition, between 0% and 100%, we used beta regression (as implemented by glmmTMB::glmmTMB()); however, the mediation modelling function mediation::mediate has troubles with beta regression, as has piecewiseSEM::psem() when it comes to fitting the full mediation model, so we employed in these cases the equivalent linear regressions (the relevant coefficient estimates and p-values are similar enough between the two to ensure that the qualitative conclusions hold). Moreover, piecewiseSEM has troubles estimating the standardized path coefficients, so we report here the unstandardized ones.

3.3.1.1 Location and family

There is a highly significant difference between the two main locations (A has higher overall performance than B; beta regression β_B-A=-0.45, p=1.45×10^-9). For those 91 participants with information about family relationships, the generation they belong to has no effect (beta regression β_old-young=-0.019, p=0.931). However, there is a slight clustering within families (the linear model with family as a random effect has an ICC of 9.9%, and including family as a fixed effect in a “flat” beta regression vs excluding it results in a p=0.016), but given the loss of sample size and the relatively small amount of variation explained we will ignore it here.

3.3.1.2 Multiple regression

A beta multiple linear regression on working memory, age, gender, years of education and location and all their interactions simplifies (using manual simplification based on F-test’s p value) to a model with main effects and two 2-way interactions only:

 Family: beta  ( logit )
Formula:          
pcr ~ age + gender + education_years + location_ab + wm_norm +  
    age:gender + age:education_years
Data: d

     AIC      BIC   logLik deviance df.resid 
  -940.6   -903.3    479.3   -958.6      454 


Dispersion parameter for beta family (): 11.5 

Conditional model:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          1.6384067  0.4358957   3.759 0.000171 ***
age                 -0.0147449  0.0084924  -1.736 0.082518 .  
genderM             -0.6487713  0.2521834  -2.573 0.010093 *  
education_years     -0.0640475  0.0389790  -1.643 0.100356    
location_abB        -0.3905770  0.0691881  -5.645 1.65e-08 ***
wm_norm              1.4110569  0.2426382   5.815 6.05e-09 ***
age:genderM          0.0171747  0.0062264   2.758 0.005809 ** 
age:education_years  0.0030296  0.0008416   3.600 0.000318 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Figure S62. Untransformed slopes with standard errors. Figure generated using R version 4.3.3 (2024-02-29)

Figure S63. Predicted values of pcr showing the interaction ofgender and age. Figure generated using R version 4.3.3 (2024-02-29)

Figure S64. Predicted values of pcr showing the interaction of education_years and age. Figure generated using R version 4.3.3 (2024-02-29)

suggesting that age and years of education have no main effects, that working memory does have a positive effect, and that males have better performance than the females but there is an interaction between education and age, and between age and gender, and that participants from A have better performance than those from B. As above, we test more complex of causality using mediation and path analysis.

3.3.1.3 Mediation: gender → education → tone

The mediation model where gender influences tone through years of education finds a highly significant positive indirect effect (ACME) and no direct effect (ADE); as fitted using mediation::mediate() (with linear regression):

Causal Mediation Analysis 

Quasi-Bayesian Confidence Intervals

               Estimate 95% CI Lower 95% CI Upper p-value    
ACME            0.03816      0.02488         0.05  <2e-16 ***
ADE            -0.00215     -0.02616         0.02  0.8598    
Total Effect    0.03600      0.00838         0.06  0.0082 ** 
Prop. Mediated  1.05577      0.61086         3.48  0.0082 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Sample Size Used: 463 


Simulations: 10000 

and as fitted using piecewiseSEM::psem() (using linear regression):

Structural Equation Model of tone_pcr_results$med_gender__education$piecewise$linearreg$model 

Call:
  education_years ~ gender_n
  pcr ~ gender_n + education_years

    AIC
 2490.046

---
Tests of directed separation:

 No independence claims present. Tests of directed separation not possible.

--
Global goodness-of-fit:

Chi-Squared = 0 with P-value = 1 and on 0 degrees of freedom
Fisher's C = NA with P-value = NA and on 0 degrees of freedom

---
Coefficients:

         Response       Predictor Estimate Std.Error  DF Crit.Value P.Value
  education_years        gender_n   0.3947    0.0388 461    10.1653  0.0000
              pcr        gender_n  -0.0020    0.0133 460    -0.1537  0.8779
              pcr education_years   0.0154    0.0015 460    10.4643  0.0000
  Std.Estimate    
             - ***
       -0.0067    
        0.4528 ***

  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05

---
Individual R-squared:

         Response     method R.squared
  education_years nagelkerke       0.2
              pcr       none       0.2

Figure S65. Mediation model of gender, years of education and pcr showing the unstandardized coefficients (fitted using piecewiseSEM::psem()); dotted arrows are not significant. Figure generated using R version 4.3.3 (2024-02-29)

Moreover, testing this partial mediation model against the full mediation model using d-separation and model comparison (N.B., using linear regression) finds that there is no direct effect of gender, but that it is entirely mediated through years of education (d-sep p=0.878, model comparison χ²(1)=0.024, p=0.877).

3.3.1.4 Mediation: age → education → tone

Likewise, fitting a mediation model where age influences tone through years of education finds a significant positive direct (ADE) and a significant negative mediated effect (ACME); as fitted using mediation::mediate() (with linear regression):

Causal Mediation Analysis 

Quasi-Bayesian Confidence Intervals

                Estimate 95% CI Lower 95% CI Upper p-value   
ACME           -0.013198    -0.025383          0.0  0.0226 * 
ADE             0.001814     0.000634          0.0  0.0016 **
Total Effect   -0.011384    -0.023323          0.0  0.0494 * 
Prop. Mediated  1.151918     0.992957          2.1  0.0268 * 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Sample Size Used: 463 


Simulations: 10000 

and as fitted using piecewiseSEM::psem() (using linear regression):

Structural Equation Model of tone_pcr_results$med_age__education$piecewise$linearreg$model 

Call:
  education_years ~ age
  pcr ~ age + education_years

    AIC
 2040.451

---
Tests of directed separation:

 No independence claims present. Tests of directed separation not possible.

--
Global goodness-of-fit:

Chi-Squared = 0 with P-value = 1 and on 0 degrees of freedom
Fisher's C = NA with P-value = NA and on 0 degrees of freedom

---
Coefficients:

         Response       Predictor Estimate Std.Error  DF Crit.Value P.Value
  education_years             age  -0.0335    0.0014 461   -23.8174  0.0000
              pcr             age   0.0018    0.0006 460     2.8325  0.0048
              pcr education_years   0.0188    0.0019 460    10.1289  0.0000
  Std.Estimate    
             - ***
        0.1543  **
        0.5516 ***

  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05

---
Individual R-squared:

         Response     method R.squared
  education_years nagelkerke      0.71
              pcr       none      0.22

Figure S66. Mediation model of age, years of education and pcr showing the unstandardized coefficients (fitted using piecewiseSEM::psem()). Figure generated using R version 4.3.3 (2024-02-29)

Moreover, the effect of age is split between the direct positive effect and the mediated (negative effect on years of education) effect (d-sep p=0.005, model comparison χ²(1)=8.0, p=0.0047).

3.3.1.5 Mediation: location → education → tone

Likewise, fitting a mediation model where location influences tone through years of education finds significant negative (i.e., that B has worse performance than A) direct (ADE) and mediated (ACME) effects; as fitted using mediation::mediate() (with linear regression):

Causal Mediation Analysis 

Quasi-Bayesian Confidence Intervals

               Estimate 95% CI Lower 95% CI Upper p-value    
ACME            -0.0218      -0.0352        -0.01   2e-04 ***
ADE             -0.0470      -0.0708        -0.02   4e-04 ***
Total Effect    -0.0688      -0.0950        -0.04  <2e-16 ***
Prop. Mediated   0.3165       0.1577         0.52   2e-04 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Sample Size Used: 463 


Simulations: 10000 

and as fitted using piecewiseSEM::psem() (using linear regression):

Structural Equation Model of tone_pcr_results$med_location__education$piecewise$linearreg$model 

Call:
  education_years ~ location_bin
  pcr ~ location_bin + education_years

    AIC
 2529.685

---
Tests of directed separation:

 No independence claims present. Tests of directed separation not possible.

--
Global goodness-of-fit:

Chi-Squared = 0 with P-value = 1 and on 0 degrees of freedom
Fisher's C = NA with P-value = NA and on 0 degrees of freedom

---
Coefficients:

         Response       Predictor Estimate Std.Error  DF Crit.Value P.Value
  education_years    location_bin  -0.2587    0.0385 461    -6.7199   0e+00
              pcr    location_bin  -0.0472    0.0120 460    -3.9431   1e-04
              pcr education_years   0.0144    0.0014 460    10.1260   0e+00
  Std.Estimate    
             - ***
       -0.1641 ***
        0.4214 ***

  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05

---
Individual R-squared:

         Response     method R.squared
  education_years nagelkerke      0.10
              pcr       none      0.23

Figure S67. Mediation model of age, years of education and pcr showing the unstandardized coefficients (fitted using piecewiseSEM::psem()). Figure generated using R version 4.3.3 (2024-02-29)

Moreover, the effect of location is split between the direct and the mediated effects (d-sep p=9.3×10^-5, model comparison χ²(1)=15.4, p=1e-04).

3.3.1.6 Mediation: education, age, gender → working memory → tone

Here we want to check if there is any effect of working memory on tone above and beyond that of education, age and gender, and we found both significant positive direct (ADE) and mediated (ACME) effects; as fitted using mediation::mediate() (with linear regression):

Causal Mediation Analysis 

Quasi-Bayesian Confidence Intervals

               Estimate 95% CI Lower 95% CI Upper p-value    
ACME            0.00360      0.00178         0.01  <2e-16 ***
ADE             0.01542      0.01152         0.02  <2e-16 ***
Total Effect    0.01902      0.01545         0.02  <2e-16 ***
Prop. Mediated  0.18836      0.09467         0.30  <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Sample Size Used: 463 


Simulations: 10000 

and as fitted using piecewiseSEM::psem() (using linear regression):

Structural Equation Model of tone_pcr_results$med_wm__all$piecewise$linearreg$model 

Call:
  wm_norm ~ education_years + gender_n + age
  pcr ~ wm_norm + education_years + gender_n + age

    AIC
 -1094.782

---
Tests of directed separation:

 No independence claims present. Tests of directed separation not possible.

--
Global goodness-of-fit:

Chi-Squared = 0 with P-value = 1 and on 0 degrees of freedom
Fisher's C = NA with P-value = NA and on 0 degrees of freedom

---
Coefficients:

  Response       Predictor Estimate Std.Error  DF Crit.Value P.Value
   wm_norm education_years   0.0198    0.0021 459     9.3059  0.0000
   wm_norm        gender_n  -0.0277    0.0146 459    -1.8919  0.0591
   wm_norm             age  -0.0061    0.0007 459    -8.5939  0.0000
       pcr         wm_norm   0.1813    0.0415 458     4.3701  0.0000
       pcr education_years   0.0154    0.0021 458     7.4803  0.0000
       pcr        gender_n  -0.0007    0.0131 458    -0.0525  0.9582
       pcr             age   0.0029    0.0007 458     4.3316  0.0000
  Std.Estimate    
        0.4191 ***
       -0.0649    
       -0.3744 ***
        0.2515 ***
        0.4528 ***
       -0.0022    
        0.2507 ***

  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05

---
Individual R-squared:

  Response method R.squared
   wm_norm   none      0.50
       pcr   none      0.25

Figure S68. Mediation model of education, age, gender, wm and pcr showing the unstandardized coefficients (fitted using piecewiseSEM::psem()). Figure generated using R version 4.3.3 (2024-02-29)

It can be seen that, indeed, working memory has a positive effect on tone while also mediating some of the effects of age and education.

3.3.1.7 Complex path model

Finally, we fitted a complex path model involving all these factors using piecewiseSEM::psem() (with linear regression):

Structural Equation Model of tone_pcr_results$path_model$linearreg$model 

Call:
  education_years ~ age + gender_n + location_bin
  age ~ location_bin + gender_n
  gender_n ~ location_bin
  wm_norm ~ education_years + gender_n + age
  pcr ~ wm_norm + age + gender_n + education_years + location_bin

    AIC
 5655.388

---
Tests of directed separation:

                Independ.Claim Test.Type  DF Crit.Value P.Value 
  wm_norm ~ location_bin + ...      coef 458     1.7014  0.0895 

--
Global goodness-of-fit:

Chi-Squared = 2.917 with P-value = 0.088 and on 1 degrees of freedom
Fisher's C = 4.826 with P-value = 0.09 and on 2 degrees of freedom

---
Coefficients:

         Response       Predictor Estimate Std.Error  DF Crit.Value P.Value
  education_years             age  -0.0318    0.0014 459   -22.4979  0.0000
  education_years        gender_n   0.2755    0.0393 459     7.0071  0.0000
  education_years    location_bin  -0.2420    0.0385 459    -6.2790  0.0000
              age    location_bin  -0.1792    1.1363 460    -0.1577  0.8747
              age        gender_n  -2.8378    1.2138 460    -2.3379  0.0198
         gender_n    location_bin  -0.0181    0.1986 461    -0.0910  0.9275
          wm_norm education_years   0.0198    0.0021 459     9.3059  0.0000
          wm_norm        gender_n  -0.0277    0.0146 459    -1.8919  0.0591
          wm_norm             age  -0.0061    0.0007 459    -8.5939  0.0000
              pcr         wm_norm   0.1942    0.0410 457     4.7385  0.0000
              pcr             age   0.0026    0.0007 457     3.8377  0.0001
              pcr        gender_n   0.0030    0.0129 457     0.2311  0.8173
              pcr education_years   0.0133    0.0021 457     6.2998  0.0000
              pcr    location_bin  -0.0470    0.0119 457    -3.9514  0.0001
  Std.Estimate    
             - ***
             - ***
             - ***
       -0.0073    
       -0.1084   *
        -0.005    
        0.4191 ***
       -0.0649    
       -0.3744 ***
        0.2693 ***
        0.2206 ***
        0.0097    
         0.389 ***
       -0.1633 ***

  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05

---
Individual R-squared:

         Response     method R.squared
  education_years nagelkerke      0.76
              age       none      0.01
         gender_n nagelkerke      0.00
          wm_norm       none      0.50
              pcr       none      0.27

Figure S69. The complex path model for pcr showing the unstandardized coefficients (fitted using piecewiseSEM::psem()). Figure generated using R version 4.3.3 (2024-02-29)

This confirms and extends the previous simpler mediation models in that pcr is directly influenced by working memory (positively), years of education (more is better), age (older is better) and location (A is better), but also indirectly by gender (mediated through years of education and age); both age and location also have effects mediated through years of education, and age and years of education have effects mediated through working memory.

3.3.1.8 Conclusions

The tone task performance as measured by the % correct total responses in the recoded dataset (aka pcr) does somewhat cluster within families (but we ignore this here due to the low sample size and variance explained), but is done better by participants from A, who are older, who have more education and who have better working memory performance, while gender has only an indirect effect.

3.3.2 d’ (recode)

We focus here on the d’ estimate (dpr = d prime recoded). Its distribution seems relatively normal and diagnostics (not shown) suggest that, indeed, a linear model fits this variable quite well. However, piecewiseSEM has troubles estimating (some of) the standardized path coefficients, so we report here the unstandardized ones.

3.3.2.1 Location and family

There is a highly significant difference between the two main locations (A has higher overall performance than B; β_B-A=-0.63, p=1.56×10^-10). For those 91 participants with information about family relationships, the generation they belong to has no effect (β_old-young=0.12, p=0.62), and, moreover, there is no clustering within families (the beta model with family as a random effect has an ICC of 4.4%), suggesting that we need not model these factors here.

3.3.2.2 Multiple regression

A beta multiple linear regression of the working memory task performance on age, gender, years of education and location and all their interactions simplifies (using manual simplification based on F-test’s p value) to a model with main effects and one interaction only:

Call:
lm(formula = dpr ~ age + gender + education_years + location_ab + 
    wm_norm + age:education_years + gender:education_years, data = d)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.82011 -0.46418  0.07605  0.60458  2.14666 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)              1.910244   0.506963   3.768 0.000186 ***
age                     -0.013304   0.010025  -1.327 0.185169    
genderM                  0.621352   0.194244   3.199 0.001476 ** 
education_years         -0.055608   0.047563  -1.169 0.242956    
location_abB            -0.481857   0.084157  -5.726 1.87e-08 ***
wm_norm                  1.639536   0.290392   5.646 2.90e-08 ***
age:education_years      0.003958   0.001014   3.901 0.000110 ***
genderM:education_years -0.090083   0.024266  -3.712 0.000231 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.8705 on 455 degrees of freedom
Multiple R-squared:  0.3593,    Adjusted R-squared:  0.3495 
F-statistic: 36.46 on 7 and 455 DF,  p-value: < 2.2e-16

Figure S70. Untransformed slopes with standard errors. Figure generated using R version 4.3.3 (2024-02-29)

Figure S71. Predicted values of dpr showing the interaction of education_years and gender. Figure generated using R version 4.3.3 (2024-02-29)

Figure S72. Predicted values of dpr showing the interaction of education_years and age. Figure generated using R version 4.3.3 (2024-02-29)

suggesting that age and years of education have no main effects, that working memory has a main positive effect, that males have better performance than the females but there is an interaction between education and age, and between education and gender, and that participants from A have better performance than those from B. As above, we test more complex of causality using mediation and path analysis.

3.3.2.3 Mediation: gender → education → tone

The mediation model where gender influences tone through years of education finds a highly significant positive indirect effect (ACME) and no direct effect (ADE); as fitted using mediation::mediate() (with linear regression):

Causal Mediation Analysis 

Quasi-Bayesian Confidence Intervals

               Estimate 95% CI Lower 95% CI Upper p-value    
ACME             0.2904       0.1874         0.40  <2e-16 ***
ADE             -0.0112      -0.1980         0.17    0.91    
Total Effect     0.2792       0.0679         0.49    0.01 *  
Prop. Mediated   1.0329       0.5950         3.32    0.01 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Sample Size Used: 463 


Simulations: 10000 

and as fitted using piecewiseSEM::psem():

Structural Equation Model of tone_dpr_results$med_gender__education$piecewise$model 

Call:
  education_years ~ gender_n
  dpr ~ gender_n + education_years

    AIC
 4349.853

---
Tests of directed separation:

 No independence claims present. Tests of directed separation not possible.

--
Global goodness-of-fit:

Chi-Squared = 0 with P-value = 1 and on 0 degrees of freedom
Fisher's C = NA with P-value = NA and on 0 degrees of freedom

---
Coefficients:

         Response       Predictor Estimate Std.Error  DF Crit.Value P.Value
  education_years        gender_n   0.3947    0.0388 461    10.1653  0.0000
              dpr        gender_n  -0.0111    0.0992 460    -0.1121  0.9108
              dpr education_years   0.1179    0.0110 460    10.7169  0.0000
  Std.Estimate    
             - ***
       -0.0048    
        0.4613 ***

  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05

---
Individual R-squared:

         Response     method R.squared
  education_years nagelkerke      0.20
              dpr       none      0.21

Figure S73. Mediation model of gender, years of education and dpr showing the unstandardized coefficients (fitted using piecewiseSEM::psem()); dotted arrows are not significant. Figure generated using R version 4.3.3 (2024-02-29)

Moreover, testing this partial mediation model against the full mediation model using d-separation and model comparison finds that there is no direct effect of gender, but that it is entirely mediated through years of education (d-sep p=0.911, model comparison χ²(1)=0.013, p=0.909).

3.3.2.4 Mediation: age → education → tone

Likewise, fitting a mediation model where age influences tone through years of education finds a significant positive direct (ADE) and a significant negative mediated effect (ACME); as fitted using mediation::mediate() (with linear regression):

Causal Mediation Analysis 

Quasi-Bayesian Confidence Intervals

               Estimate 95% CI Lower 95% CI Upper p-value    
ACME           -0.10432     -0.19937        -0.01  0.0242 *  
ADE             0.01632      0.00703         0.03  0.0002 ***
Total Effect   -0.08800     -0.18221         0.00  0.0572 .  
Prop. Mediated  1.17639      0.59488         2.30  0.0330 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Sample Size Used: 463 


Simulations: 10000 

and as fitted using piecewiseSEM::psem():

Structural Equation Model of tone_dpr_results$med_age__education$piecewise$model 

Call:
  education_years ~ age
  dpr ~ age + education_years

    AIC
 3896.584

---
Tests of directed separation:

 No independence claims present. Tests of directed separation not possible.

--
Global goodness-of-fit:

Chi-Squared = 0 with P-value = 1 and on 0 degrees of freedom
Fisher's C = NA with P-value = NA and on 0 degrees of freedom

---
Coefficients:

         Response       Predictor Estimate Std.Error  DF Crit.Value P.Value
  education_years             age  -0.0335    0.0014 461   -23.8174   0e+00
              dpr             age   0.0163    0.0047 460     3.4264   7e-04
              dpr education_years   0.1484    0.0138 460    10.7601   0e+00
  Std.Estimate    
             - ***
        0.1849 ***
        0.5807 ***

  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05

---
Individual R-squared:

         Response     method R.squared
  education_years nagelkerke      0.71
              dpr       none      0.23

Figure S74. Mediation model of age, years of education and dpr showing the unstandardized coefficients (fitted using piecewiseSEM::psem()). Figure generated using R version 4.3.3 (2024-02-29)

Moreover, the effect of age is split between the direct positive effect and the mediated (negative effect on years of education) effect (d-sep p=6.66×10^-4, model comparison χ²(1)=11.7, p=6e-04).

3.3.2.5 Mediation: location → education → tone

Likewise, fitting a mediation model where location influences tone through years of education finds significant negative (i.e., that B has worse performance than A) direct (ADE) and mediated (ACME) effects; as fitted using mediation::mediate() (with linear regression):

Causal Mediation Analysis 

Quasi-Bayesian Confidence Intervals

               Estimate 95% CI Lower 95% CI Upper p-value    
ACME             -0.164       -0.261        -0.07   2e-04 ***
ADE              -0.466       -0.633        -0.30  <2e-16 ***
Total Effect     -0.629       -0.819        -0.44  <2e-16 ***
Prop. Mediated    0.259        0.131         0.40   2e-04 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Sample Size Used: 463 


Simulations: 10000 

and as fitted using piecewiseSEM::psem():

Structural Equation Model of tone_dpr_results$med_location__education$piecewise$model 

Call:
  education_years ~ location_bin
  dpr ~ location_bin + education_years

    AIC
 4377.625

---
Tests of directed separation:

 No independence claims present. Tests of directed separation not possible.

--
Global goodness-of-fit:

Chi-Squared = 0 with P-value = 1 and on 0 degrees of freedom
Fisher's C = NA with P-value = NA and on 0 degrees of freedom

---
Coefficients:

         Response       Predictor Estimate Std.Error  DF Crit.Value P.Value
  education_years    location_bin  -0.2587    0.0385 461    -6.7199       0
              dpr    location_bin  -0.4654    0.0881 460    -5.2803       0
              dpr education_years   0.1076    0.0104 460    10.3027       0
  Std.Estimate    
             - ***
       -0.2158 ***
        0.4211 ***

  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05

---
Individual R-squared:

         Response     method R.squared
  education_years nagelkerke      0.10
              dpr       none      0.26

Figure S75. Mediation model of age, years of education and dpr showing the unstandardized coefficients (fitted using piecewiseSEM::psem()). Figure generated using R version 4.3.3 (2024-02-29)

Moreover, the effect of location is split between the direct and the mediated effects (d-sep p=1.99×10^-7, model comparison χ²(1)=27.2, p=0).

3.3.2.6 Mediation: education, age, gender → working memory → tone

Here we want to check if there is any effect of working memory on tone above and beyond that of education, age and gender, and we found both significant positive direct (ADE) and mediated (ACME) effects; as fitted using mediation::mediate() (with linear regression):

Causal Mediation Analysis 

Quasi-Bayesian Confidence Intervals

               Estimate 95% CI Lower 95% CI Upper p-value    
ACME             0.0307       0.0170         0.05  <2e-16 ***
ADE              0.1195       0.0901         0.15  <2e-16 ***
Total Effect     0.1502       0.1229         0.18  <2e-16 ***
Prop. Mediated   0.2034       0.1120         0.31  <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Sample Size Used: 463 


Simulations: 10000 

and as fitted using piecewiseSEM::psem():

Structural Equation Model of psem_dpr_wm 

Call:
  wm_norm ~ education_years + gender_n + age
  dpr ~ wm_norm + education_years + gender_n + age

    AIC
 755.093

---
Tests of directed separation:

 No independence claims present. Tests of directed separation not possible.

--
Global goodness-of-fit:

Chi-Squared = 0 with P-value = 1 and on 0 degrees of freedom
Fisher's C = NA with P-value = NA and on 0 degrees of freedom

---
Coefficients:

  Response       Predictor Estimate Std.Error  DF Crit.Value P.Value
   wm_norm education_years   0.0198    0.0021 459     9.3059  0.0000
   wm_norm        gender_n  -0.0277    0.0146 459    -1.8919  0.0591
   wm_norm             age  -0.0061    0.0007 459    -8.5939  0.0000
       dpr         wm_norm   1.5471    0.3059 458     5.0572  0.0000
       dpr education_years   0.1194    0.0152 458     7.8449  0.0000
       dpr        gender_n  -0.0011    0.0963 458    -0.0113  0.9910
       dpr             age   0.0259    0.0050 458     5.1699  0.0000
  Std.Estimate    
        0.4191 ***
       -0.0649    
       -0.3744 ***
        0.2865 ***
        0.4674 ***
       -0.0005    
        0.2945 ***

  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05

---
Individual R-squared:

  Response method R.squared
   wm_norm   none      0.50
       dpr   none      0.27

Figure S76. Mediation model of education, age, gender, wm and dpr showing the unstandardized coefficients (fitted using piecewiseSEM::psem()). Figure generated using R version 4.3.3 (2024-02-29)

It can be seen that, indeed, working memory has a positive effect on tone while also mediating some of the effects of age and education.

3.3.2.7 Complex path model

Finally, we fitted a complex path model involving all these factors using piecewiseSEM::psem():

Structural Equation Model of psem_dpr_path 

Call:
  education_years ~ age + gender_n + location_bin
  age ~ location_bin + gender_n
  gender_n ~ location_bin
  wm_norm ~ education_years + gender_n + age
  dpr ~ wm_norm + age + gender_n + education_years + location_bin

    AIC
 7492.614

---
Tests of directed separation:

                Independ.Claim Test.Type  DF Crit.Value P.Value 
  wm_norm ~ location_bin + ...      coef 458     1.7014  0.0895 

--
Global goodness-of-fit:

Chi-Squared = 2.917 with P-value = 0.088 and on 1 degrees of freedom
Fisher's C = 4.826 with P-value = 0.09 and on 2 degrees of freedom

---
Coefficients:

         Response       Predictor Estimate Std.Error  DF Crit.Value P.Value
  education_years             age  -0.0318    0.0014 459   -22.4979  0.0000
  education_years        gender_n   0.2755    0.0393 459     7.0071  0.0000
  education_years    location_bin  -0.2420    0.0385 459    -6.2790  0.0000
              age    location_bin  -0.1792    1.1363 460    -0.1577  0.8747
              age        gender_n  -2.8378    1.2138 460    -2.3379  0.0198
         gender_n    location_bin  -0.0181    0.1986 461    -0.0910  0.9275
          wm_norm education_years   0.0198    0.0021 459     9.3059  0.0000
          wm_norm        gender_n  -0.0277    0.0146 459    -1.8919  0.0591
          wm_norm             age  -0.0061    0.0007 459    -8.5939  0.0000
              dpr         wm_norm   1.6736    0.2980 457     5.6161  0.0000
              dpr             age   0.0224    0.0049 457     4.5670  0.0000
              dpr        gender_n   0.0351    0.0938 457     0.3739  0.7087
              dpr education_years   0.0980    0.0153 457     6.3950  0.0000
              dpr    location_bin  -0.4637    0.0865 457    -5.3576  0.0000
  Std.Estimate    
             - ***
             - ***
             - ***
       -0.0073    
       -0.1084   *
        -0.005    
        0.4191 ***
       -0.0649    
       -0.3744 ***
        0.3099 ***
        0.2549 ***
        0.0152    
        0.3833 ***
        -0.215 ***

  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05

---
Individual R-squared:

         Response     method R.squared
  education_years nagelkerke      0.76
              age       none      0.01
         gender_n nagelkerke      0.00
          wm_norm       none      0.50
              dpr       none      0.32

Figure S77. The complex path model for dpr showing the unstandardized coefficients (fitted using piecewiseSEM::psem()). Figure generated using R version 4.3.3 (2024-02-29)

This confirms and extends the previous simpler mediation models in that dpr is directly influenced by working memory (positively), years of education (more is better), age (older is better) and location (A is better), but also indirectly by gender (mediated through years of education and age); both age and location also have effects mediated through years of education, and age and years of education have effects mediated through working memory.

3.3.2.8 Conclusions

As expected, d’ and % correct total responses in the recoded dataset behave very similarly, down to very similar coefficient estimates and associated p-values. We compared (using AIC) the two measures (a rather “apples to pears” comparison in this case) using the multiple regression model ΔAIC(%cr, d’) = -2136.0, and the complex path model ΔAIC(%cr, d’) = -1837.2, both suggesting that %cr fits the data much better than d’.

3.3.3 Secondary measures

Here we look at secondary measures.

3.3.3.1 % correct total (original)

We focus here on the % total correct responses on the ‘original’ dataset estimate (pco = percent correct original) – see the comments above for pcr.

3.3.3.1.1 Location and family

There is a highly significant difference between the two main locations (A has higher overall performance than B; beta regression β_B-A=-0.3, p=2.73×10^-7). For those 91 participants with information about family relationships, the generation they belong to has no effect (beta regression β_old-young=0.07, p=0.619) and there is no clustering within families (the linear model with family as a random effect has an ICC of 9.8%, and including family as a fixed effect in a “flat” beta regression vs excluding it results in a p=0.295).

3.3.3.1.2 Multiple regression

 Family: beta  ( logit )
Formula:          
pco ~ age + gender + education_years + location_ab + wm_norm +  
    gender:education_years + education_years:wm_norm
Data: d

     AIC      BIC   logLik deviance df.resid 
  -909.2   -872.0    463.6   -927.2      454 


Dispersion parameter for beta family (): 17.8 

Conditional model:
                         Estimate Std. Error z value Pr(>|z|)    
(Intercept)             -0.037382   0.199472  -0.187 0.851344    
age                      0.010988   0.003276   3.354 0.000796 ***
genderM                  0.336120   0.122824   2.737 0.006208 ** 
education_years          0.116932   0.016936   6.904 5.04e-12 ***
location_abB            -0.243294   0.053010  -4.590 4.44e-06 ***
wm_norm                  1.608457   0.298826   5.383 7.34e-08 ***
genderM:education_years -0.052539   0.015700  -3.346 0.000819 ***
education_years:wm_norm -0.107255   0.034818  -3.080 0.002067 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Figure S78. Untransformed slopes with standard errors. Figure generated using R version 4.3.3 (2024-02-29)

Figure S79. Predicted values of pco showing the interaction ofgender and age. Figure generated using R version 4.3.3 (2024-02-29)

Figure S80. Predicted values of pco showing the interaction of education_years and age. Figure generated using R version 4.3.3 (2024-02-29)

3.3.3.1.3 Mediation: gender → education → tone

Causal Mediation Analysis 

Quasi-Bayesian Confidence Intervals

               Estimate 95% CI Lower 95% CI Upper p-value    
ACME            0.03391      0.02207         0.05  <2e-16 ***
ADE            -0.00220     -0.02378         0.02    0.84    
Total Effect    0.03171      0.00673         0.06    0.01 ** 
Prop. Mediated  1.06049      0.61053         3.69    0.01 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Sample Size Used: 463 


Simulations: 10000 

Structural Equation Model of tone_pco_results$med_gender__education$piecewise$linearreg$model 

Call:
  education_years ~ gender_n
  pco ~ gender_n + education_years

    AIC
 2376.695

---
Tests of directed separation:

 No independence claims present. Tests of directed separation not possible.

--
Global goodness-of-fit:

Chi-Squared = 0 with P-value = 1 and on 0 degrees of freedom
Fisher's C = NA with P-value = NA and on 0 degrees of freedom

---
Coefficients:

         Response       Predictor Estimate Std.Error  DF Crit.Value P.Value
  education_years        gender_n   0.3947    0.0388 461    10.1653  0.0000
              pco        gender_n  -0.0022    0.0118 460    -0.1836  0.8544
              pco education_years   0.0137    0.0013 460    10.5265  0.0000
  Std.Estimate    
             - ***
       -0.0079    
         0.455 ***

  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05

---
Individual R-squared:

         Response     method R.squared
  education_years nagelkerke      0.20
              pco       none      0.21

Figure S81. Mediation model. Figure generated using R version 4.3.3 (2024-02-29)

d-sep p=0.854, model comparison χ²(1)=0.034, p=0.854

3.3.3.1.4 Mediation: age → education → tone

Causal Mediation Analysis 

Quasi-Bayesian Confidence Intervals

                Estimate 95% CI Lower 95% CI Upper p-value   
ACME           -0.011765    -0.022675         0.00  0.0202 * 
ADE             0.001689     0.000639         0.00  0.0028 **
Total Effect   -0.010077    -0.020841         0.00  0.0486 * 
Prop. Mediated  1.160614     0.986975         2.16  0.0286 * 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Sample Size Used: 463 


Simulations: 10000 

Structural Equation Model of tone_pco_results$med_age__education$piecewise$linearreg$model 

Call:
  education_years ~ age
  pco ~ age + education_years

    AIC
 1926.169

---
Tests of directed separation:

 No independence claims present. Tests of directed separation not possible.

--
Global goodness-of-fit:

Chi-Squared = 0 with P-value = 1 and on 0 degrees of freedom
Fisher's C = NA with P-value = NA and on 0 degrees of freedom

---
Coefficients:

         Response       Predictor Estimate Std.Error  DF Crit.Value P.Value
  education_years             age  -0.0335    0.0014 461   -23.8174  0.0000
              pco             age   0.0017    0.0006 460     2.9957  0.0029
              pco education_years   0.0169    0.0016 460    10.2867  0.0000
  Std.Estimate    
             - ***
        0.1628  **
        0.5591 ***

  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05

---
Individual R-squared:

         Response     method R.squared
  education_years nagelkerke      0.71
              pco       none      0.22

Figure S82. Mediation model. Figure generated using R version 4.3.3 (2024-02-29)

d-sep p=0.003, model comparison χ²(1)=8.9, p=0.0028.

3.3.3.1.5 Mediation: location → education → tone

Causal Mediation Analysis 

Quasi-Bayesian Confidence Intervals

               Estimate 95% CI Lower 95% CI Upper p-value    
ACME            -0.0194      -0.0309        -0.01   8e-04 ***
ADE             -0.0403      -0.0607        -0.02   2e-04 ***
Total Effect    -0.0597      -0.0827        -0.04  <2e-16 ***
Prop. Mediated   0.3235       0.1610         0.53   8e-04 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Sample Size Used: 463 


Simulations: 10000 

Structural Equation Model of tone_pco_results$med_location__education$piecewise$linearreg$model 

Call:
  education_years ~ location_bin
  pco ~ location_bin + education_years

    AIC
 2417.395

---
Tests of directed separation:

 No independence claims present. Tests of directed separation not possible.

--
Global goodness-of-fit:

Chi-Squared = 0 with P-value = 1 and on 0 degrees of freedom
Fisher's C = NA with P-value = NA and on 0 degrees of freedom

---
Coefficients:

         Response       Predictor Estimate Std.Error  DF Crit.Value P.Value
  education_years    location_bin  -0.2587    0.0385 461    -6.7199   0e+00
              pco    location_bin  -0.0404    0.0106 460    -3.8039   2e-04
              pco education_years   0.0128    0.0013 460    10.1948   0e+00
  Std.Estimate    
             - ***
       -0.1583 ***
        0.4243 ***

  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05

---
Individual R-squared:

         Response     method R.squared
  education_years nagelkerke      0.10
              pco       none      0.23

Figure S83. Mediation model. Figure generated using R version 4.3.3 (2024-02-29)

d-sep p=1.62×10^-4, model comparison χ²(1)=14.3, p=2e-04

3.3.3.1.6 Mediation: education, age, gender → working memory → tone

Causal Mediation Analysis 

Quasi-Bayesian Confidence Intervals

               Estimate 95% CI Lower 95% CI Upper p-value    
ACME            0.00324      0.00165         0.01  <2e-16 ***
ADE             0.01385      0.01047         0.02  <2e-16 ***
Total Effect    0.01709      0.01395         0.02  <2e-16 ***
Prop. Mediated  0.18824      0.09633         0.30  <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Sample Size Used: 463 


Simulations: 10000 

Structural Equation Model of tone_pco_results$med_wm__all$piecewise$linearreg$model 

Call:
  wm_norm ~ education_years + gender_n + age
  pco ~ wm_norm + education_years + gender_n + age

    AIC
 -1209.875

---
Tests of directed separation:

 No independence claims present. Tests of directed separation not possible.

--
Global goodness-of-fit:

Chi-Squared = 0 with P-value = 1 and on 0 degrees of freedom
Fisher's C = NA with P-value = NA and on 0 degrees of freedom

---
Coefficients:

  Response       Predictor Estimate Std.Error  DF Crit.Value P.Value
   wm_norm education_years   0.0198    0.0021 459     9.3059  0.0000
   wm_norm        gender_n  -0.0277    0.0146 459    -1.8919  0.0591
   wm_norm             age  -0.0061    0.0007 459    -8.5939  0.0000
       pco         wm_norm   0.1634    0.0366 458     4.4599  0.0000
       pco education_years   0.0139    0.0018 458     7.6064  0.0000
       pco        gender_n  -0.0011    0.0115 458    -0.0920  0.9268
       pco             age   0.0027    0.0006 458     4.5253  0.0000
  Std.Estimate    
        0.4191 ***
       -0.0649    
       -0.3744 ***
        0.2559 ***
        0.4591 ***
       -0.0039    
        0.2611 ***

  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05

---
Individual R-squared:

  Response method R.squared
   wm_norm   none      0.50
       pco   none      0.25

Figure S84. Mediation model. Figure generated using R version 4.3.3 (2024-02-29)

3.3.3.1.7 Complex path model

Structural Equation Model of tone_pco_results$path_model$linearreg$model 

Call:
  education_years ~ age + gender_n + location_bin
  age ~ location_bin + gender_n
  gender_n ~ location_bin
  wm_norm ~ education_years + gender_n + age
  pco ~ wm_norm + age + gender_n + education_years + location_bin

    AIC
 5541.539

---
Tests of directed separation:

                Independ.Claim Test.Type  DF Crit.Value P.Value 
  wm_norm ~ location_bin + ...      coef 458     1.7014  0.0895 

--
Global goodness-of-fit:

Chi-Squared = 2.917 with P-value = 0.088 and on 1 degrees of freedom
Fisher's C = 4.826 with P-value = 0.09 and on 2 degrees of freedom

---
Coefficients:

         Response       Predictor Estimate Std.Error  DF Crit.Value P.Value
  education_years             age  -0.0318    0.0014 459   -22.4979  0.0000
  education_years        gender_n   0.2755    0.0393 459     7.0071  0.0000
  education_years    location_bin  -0.2420    0.0385 459    -6.2790  0.0000
              age    location_bin  -0.1792    1.1363 460    -0.1577  0.8747
              age        gender_n  -2.8378    1.2138 460    -2.3379  0.0198
         gender_n    location_bin  -0.0181    0.1986 461    -0.0910  0.9275
          wm_norm education_years   0.0198    0.0021 459     9.3059  0.0000
          wm_norm        gender_n  -0.0277    0.0146 459    -1.8919  0.0591
          wm_norm             age  -0.0061    0.0007 459    -8.5939  0.0000
              pco         wm_norm   0.1743    0.0362 457     4.8103  0.0000
              pco             age   0.0024    0.0006 457     4.0484  0.0001
              pco        gender_n   0.0020    0.0114 457     0.1795  0.8577
              pco education_years   0.0120    0.0019 457     6.4562  0.0000
              pco    location_bin  -0.0399    0.0105 457    -3.7875  0.0002
  Std.Estimate    
             - ***
             - ***
             - ***
       -0.0073    
       -0.1084   *
        -0.005    
        0.4191 ***
       -0.0649    
       -0.3744 ***
        0.2729 ***
        0.2323 ***
        0.0075    
         0.398 ***
       -0.1563 ***

  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05

---
Individual R-squared:

         Response     method R.squared
  education_years nagelkerke      0.76
              age       none      0.01
         gender_n nagelkerke      0.00
          wm_norm       none      0.50
              pco       none      0.28

Figure S85. The complex path model. Figure generated using R version 4.3.3 (2024-02-29)

3.3.3.1.8 Conclusions

pco behaves relatively similarly to pcr, and we compared (using AIC) the two (more meaningful here) using the multiple regression model ΔAIC(pcr, pco) = -31.3, and the complex path model ΔAIC(pcr, pco) = 113.8, both suggesting that pcr (the ‘reduced’ dataset) fits the data better than pco (the ‘original’ dataset).

3.3.3.2 d’ (original)

We focus here on d’ on the ‘original’ dataset estimate (dpo = d prime original) – see the comments above for dpr.

3.3.3.2.1 location and family

There is a highly significant difference between the two main locations (A has higher overall performance than B; β_B-A=-0.63, p=1.56×10^-10). For those 91 participants with information about family relationships, the generation they belong to has no effect (β_old-young=0.12, p=0.62), and, moreover, there is no clustering within families (the beta model with family as a random effect has an ICC of 4.4%), suggesting that we need not model these factors here.

3.3.3.2.2 Multiple regression

Call:
lm(formula = dpo ~ age + gender + education_years + location_ab + 
    wm_norm + age:education_years + gender:education_years, data = d)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.53839 -0.35857  0.08772  0.51313  1.87452 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)              1.4281037  0.4467995   3.196 0.001489 ** 
age                     -0.0075783  0.0088356  -0.858 0.391504    
genderM                  0.5576186  0.1711920   3.257 0.001209 ** 
education_years         -0.0297572  0.0419181  -0.710 0.478137    
location_abB            -0.3529232  0.0741694  -4.758 2.63e-06 ***
wm_norm                  1.4759196  0.2559303   5.767 1.49e-08 ***
age:education_years      0.0030647  0.0008941   3.428 0.000664 ***
genderM:education_years -0.0730950  0.0213866  -3.418 0.000688 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.7672 on 455 degrees of freedom
Multiple R-squared:  0.3526,    Adjusted R-squared:  0.3427 
F-statistic: 35.41 on 7 and 455 DF,  p-value: < 2.2e-16

Figure S86. Untransformed slopes with standard errors. Figure generated using R version 4.3.3 (2024-02-29)

Figure S87. Predicted values of dpo showing the interaction ofgender and age. Figure generated using R version 4.3.3 (2024-02-29)

Figure S88. Predicted values of dpo showing the interaction of education_years and age. Figure generated using R version 4.3.3 (2024-02-29)

3.3.3.2.3 Mediation gender → education → tone

Causal Mediation Analysis 

Quasi-Bayesian Confidence Intervals

               Estimate 95% CI Lower 95% CI Upper p-value    
ACME             0.2583       0.1664         0.36  <2e-16 ***
ADE              0.0425      -0.1220         0.21  0.6134    
Total Effect     0.3008       0.1122         0.49  0.0014 ** 
Prop. Mediated   0.8609       0.5256         2.04  0.0014 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Sample Size Used: 463 


Simulations: 10000 

Structural Equation Model of tone_dpo_results$med_gender__education$piecewise$model 

Call:
  education_years ~ gender_n
  dpo ~ gender_n + education_years

    AIC
 4220.805

---
Tests of directed separation:

 No independence claims present. Tests of directed separation not possible.

--
Global goodness-of-fit:

Chi-Squared = 0 with P-value = 1 and on 0 degrees of freedom
Fisher's C = NA with P-value = NA and on 0 degrees of freedom

---
Coefficients:

         Response       Predictor Estimate Std.Error  DF Crit.Value P.Value
  education_years        gender_n   0.3947    0.0388 461    10.1653   0.000
              dpo        gender_n   0.0427    0.0863 460     0.4948   0.621
              dpo education_years   0.1046    0.0096 460    10.9325   0.000
  Std.Estimate    
             - ***
        0.0211    
        0.4669 ***

  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05

---
Individual R-squared:

         Response     method R.squared
  education_years nagelkerke      0.20
              dpo       none      0.22

Figure S89. Mediation model. Figure generated using R version 4.3.3 (2024-02-29)

d-sep p=0.621, model comparison χ²(1)=0.25, p=0.62

3.3.3.2.4 Mediation age → education → tone

Causal Mediation Analysis 

Quasi-Bayesian Confidence Intervals

               Estimate 95% CI Lower 95% CI Upper p-value    
ACME           -0.09232     -0.17748        -0.01  0.0250 *  
ADE             0.01412      0.00629         0.02  0.0004 ***
Total Effect   -0.07820     -0.16227         0.00  0.0544 .  
Prop. Mediated  1.17229      0.65176         2.14  0.0294 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Sample Size Used: 463 


Simulations: 10000 

       Length Class      Mode   
model  3      psem       list   
dsep.p 1      -none-     numeric
anova  1      anova.psem list   

Figure S90. Mediation model. Figure generated using R version 4.3.3 (2024-02-29)

d-sep p=6.86×10^-4, model comparison χ²(1)=11.6, p=7e-04.

3.3.3.2.5 Mediation location → education → tone

Causal Mediation Analysis 

Quasi-Bayesian Confidence Intervals

               Estimate 95% CI Lower 95% CI Upper p-value    
ACME             -0.150       -0.239        -0.07   4e-04 ***
ADE              -0.336       -0.486        -0.19  <2e-16 ***
Total Effect     -0.487       -0.659        -0.32  <2e-16 ***
Prop. Mediated    0.307        0.159         0.48   4e-04 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Sample Size Used: 463 


Simulations: 10000 

       Length Class      Mode   
model  3      psem       list   
dsep.p 1      -none-     numeric
anova  1      anova.psem list   

Figure S91. Mediation model. Figure generated using R version 4.3.3 (2024-02-29)

d-sep p=1.62×10^-5, model comparison χ²(1)=18.7, p=0

3.3.3.2.6 Mediation education, age, gender → working memory → tone

Causal Mediation Analysis 

Quasi-Bayesian Confidence Intervals

               Estimate 95% CI Lower 95% CI Upper p-value    
ACME             0.0280       0.0164         0.04  <2e-16 ***
ADE              0.1040       0.0785         0.13  <2e-16 ***
Total Effect     0.1320       0.1082         0.16  <2e-16 ***
Prop. Mediated   0.2110       0.1214         0.32  <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Sample Size Used: 463 


Simulations: 10000 

        Length Class        Mode
model   3      psem         list
summary 8      summary.psem list

Figure S92. Mediation model. Figure generated using R version 4.3.3 (2024-02-29)

3.3.3.2.7 Complex path model

Structural Equation Model of psem_dpo_path 

Call:
  education_years ~ age + gender_n + location_bin
  age ~ location_bin + gender_n
  gender_n ~ location_bin
  wm_norm ~ education_years + gender_n + age
  dpo ~ wm_norm + age + gender_n + education_years + location_bin

    AIC
 7369.870

---
Tests of directed separation:

                Independ.Claim Test.Type  DF Crit.Value P.Value 
  wm_norm ~ location_bin + ...      coef 458     1.7014  0.0895 

--
Global goodness-of-fit:

Chi-Squared = 2.917 with P-value = 0.088 and on 1 degrees of freedom
Fisher's C = 4.826 with P-value = 0.09 and on 2 degrees of freedom

---
Coefficients:

         Response       Predictor Estimate Std.Error  DF Crit.Value P.Value
  education_years             age  -0.0318    0.0014 459   -22.4979  0.0000
  education_years        gender_n   0.2755    0.0393 459     7.0071  0.0000
  education_years    location_bin  -0.2420    0.0385 459    -6.2790  0.0000
              age    location_bin  -0.1792    1.1363 460    -0.1577  0.8747
              age        gender_n  -2.8378    1.2138 460    -2.3379  0.0198
         gender_n    location_bin  -0.0181    0.1986 461    -0.0910  0.9275
          wm_norm education_years   0.0198    0.0021 459     9.3059  0.0000
          wm_norm        gender_n  -0.0277    0.0146 459    -1.8919  0.0591
          wm_norm             age  -0.0061    0.0007 459    -8.5939  0.0000
              dpo         wm_norm   1.5057    0.2610 457     5.7688  0.0000
              dpo             age   0.0201    0.0043 457     4.6820  0.0000
              dpo        gender_n   0.0802    0.0821 457     0.9763  0.3294
              dpo education_years   0.0884    0.0134 457     6.5909  0.0000
              dpo    location_bin  -0.3379    0.0758 457    -4.4585  0.0000
  Std.Estimate    
             - ***
             - ***
             - ***
       -0.0073    
       -0.1084   *
        -0.005    
        0.4191 ***
       -0.0649    
       -0.3744 ***
         0.318 ***
         0.261 ***
        0.0397    
        0.3947 ***
       -0.1787 ***

  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05

---
Individual R-squared:

         Response     method R.squared
  education_years nagelkerke      0.76
              age       none      0.01
         gender_n nagelkerke      0.00
          wm_norm       none      0.50
              dpo       none      0.32

Figure S93. The complex path model. Figure generated using R version 4.3.3 (2024-02-29)

3.3.3.2.8 Conclusions

dpo behaves relatively similarly to dpr, and we compared (using AIC) the two (more meaningful here) using the multiple regression model ΔAIC(dpr, dpo) = 117.0, and the complex path model ΔAIC(dpr, dpo) = 122.7, both suggesting that dpr (the ‘reduced’ dataset) fits the data worse than dpo (the ‘original’ dataset). We also compared dpo and pco using the multiple regression model ΔAIC(%cr, d’) = -1987.7, and the complex path model ΔAIC(%cr, d’) = -1828.3, both suggesting that %cr fits the data much better than d’.

Finally, let’s compare both measures on both datasets:

Table S26. Model comparison (using AIC) between both measures on both datasets.

Measure 1	Measure 2	ΔAIC(multiple regression)	ΔAIC(complex path model)
pcr	dpr	-2136.0	-1837.2
pcr	pco	-31.3	113.8
pcr	dpo	-2019.0	-1714.5
dpr	pco	2104.7	1951.1
dpr	dpo	117.0	122.7
pco	dpo	-1987.7	-1828.3

It can be seen that the ordering (taking into account various caveats) is: pcr > pco >> dpo > dpr, suggesting that, indeed, the % correct responses on the ‘recoded’ dataset might be the best choice.

3.3.3.3 Bias c (recoded)

The bias c on the ‘recoded’ dataset (cbr from c bias recoded) shows some clustering within families (18.2%), but no effects of generation nor of location. While the clustering within families is potentially interesting (and might suggest that the bias has a familial component, which may have shared environmental and/or genetic components) we cannot really model it here due to the massive loss of data (dropping from 489 to 91 participants).

Interestingly, including all the potential predictors (and their interactions) in a multiple linear regression results in a model that is not better than the null model with no predictor (F(31)=1.1, p=0.377), suggesting that the bias c is idiosyncratic, probably influenced by personality, genetic and/or cultural variables that we did not measure here.

3.3.3.4 Bias c (original)

The bias c on the ‘original’ dataset (cbo from c bias original) shows some clustering within families (12.3%), but no effects of generation nor of location. As above, while the clustering within families is potentially interesting we cannot really model it here due to the massive loss of data.

Including all the potential predictors (and their interactions) in a multiple linear regression model results, after manual simplification, in only years of education having a positive significant effect (i.e., participants with more years of education have a tendency to answer ‘same’ which might simply say something about how they treat the “weird” items):

Call:
lm(formula = cbo ~ education_years, data = d)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.98951 -0.25221  0.00983  0.24083  1.24920 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)     0.389935   0.028387  13.736  < 2e-16 ***
education_years 0.020921   0.003914   5.345 1.43e-07 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.3553 on 461 degrees of freedom
Multiple R-squared:  0.05835,   Adjusted R-squared:  0.0563 
F-statistic: 28.56 on 1 and 461 DF,  p-value: 1.428e-07

3.3.4 Comparing the “original”, “recoded” and “removed” results

Here we compare side-by-side the complex path models (with linear instead of Beta regressions and showing the unstandardized coefficients) for the original (“o”), recoded (“r”) and removed (“x”) datasets.

3.3.4.1 % correct responses

Figure S94. The complex path model for pco. Figure generated using R version 4.3.3 (2024-02-29)

Figure S95. The complex path model for pcr. Figure generated using R version 4.3.3 (2024-02-29)

Figure S96. The complex path model for pcx. Figure generated using R version 4.3.3 (2024-02-29)

3.3.4.2 d’

Figure S97. The complex path model for dpo. Figure generated using R version 4.3.3 (2024-02-29)

Figure S98. The complex path model for dpr. Figure generated using R version 4.3.3 (2024-02-29)

Figure S99. The complex path model for dpx. Figure generated using R version 4.3.3 (2024-02-29)

3.3.5 Conclusions

Putting everything together, it seems that:

• on the ‘recoded’ dataset (i.e., with the “weird” items recoded as ‘same’ items), both measures of performance, the % total correct responses and the bias-free d’, behave in very similar ways, namely:

  • there is no family clustering and no effect of generation (but there is very little data to draw solid conclusions),
  • multiple regression suggests that participants from A perform better across the board, as do males and participants with a higher working memory, while age and years of education have no main effects but are involved in interactions,
  • mediation analyses dissect these, showing that:
    • gender has no direct effect but is fully mediated by education, resulting in a positive total effect,
    • age has both a positive direct effect, and a negative indirect effect (mediated through education), resulting in a very weak negative (formally ns) total effect,
    • A’s better overall performance than that of B (total effect) is due both to a mediated effect (through education, higher by about 1.5 years) and to a strong direct effect (participants from A do the task better),
    • working memory has an own positive effect and also mediates those of age and education,
  • the complex path model confirms these and suggests that the performance on the tone task is simultaneously influenced by multiple factors, directly and indirectly, in particular being increased for the participants with a higher working memory, from A, more educated and/or older.

• on the ‘original’ dataset, the two measure also behave similarly

• the % total correct responses on the ‘reduced’ dataset fits the data best (in terms of AIC), so, all in all, we should probably take it as our primary measure.

4 Appendices

4.1 Appendix I

Here we give some technical details, also citing the most important methodological packages we use in this paper.

PCA: we used prcomp(...) in package stats to estimate the PCs (which uses the singular value decomposition method), and fviz_eig(...) and fviz_pca_var(...) in package factoextra (Kassambara & Mundt, 2020).

EFA: we implemented the model fit using cfa(...) and the fit measures using fitMeasures(...) from package lavaan (Rosseel, 2012), and we plotted the fitted models using lavaanPlot(...) from package lavaanPlot (Lishinski, 2021). The preliminary EFA tests use KMO(...) in package psych (William Revelle, 2023), cortest.bartlett(...) also in package psych, and det(cor(...)); the most likely number of latent factors uses several methods implemented by fa.parallel(...) and nfactors(...) in package psych.

CFA: uses factanal(...) from package stats (with the “promax” rotation) and the resulting model was plotted using fa.diagram(...) in package psych.

Mokken: we estimated the Guttman errors using check.errors (...), the scalability coefficients H with coefH(...) and aisp with aisp(...), all in the package mokken (Ark, 2007).

SDT: the cumulative density function (cdf) of the normal distribution was estimated using qnorm(...) in package stats; we estimated d’, β, c, A’ and B’’_D using dprime(..., adjusted=TRUE) in package psycho (Makowski, 2018) with the adjustment for extreme values. Please see here and here for visual, non-technical explanations of STD.

Regression: we tested the clustering by family using mixed-effects models as implemented by lmer(...) in package lme4 (Bates, Mächler, Bolker, & Walker, 2015) with p-values as implemented in package lmerTest (Kuznetsova, Brockhoff, & Christensen, 2017), and the intra-class correlation coefficient (ICC) was estimated using icc(...) in package performance (Lüdecke, Ben-Shachar, Patil, Waggoner, & Makowski, 2021). Logistic regression is implemented by glm(..., family=binomial("logit")) or glmer(..., family=binomial("logit")) (as appropriate), linear regression by lm(...) or lmer(...), Poisson regression by glm(..., family=poisson()), and Beta regression bu glmmTMB(..., family=beta_family()) from package glmmTMB (Brooks et al., 2017).

Mediation: we used mediate(...) in package mediation (Tingley, Yamamoto, Hirose, Keele, & Imai, 2014) with 10,000 simulations and heteroskedasticity-consistent standard errors for “classic” mediation modeling, and psem(...) and dSep(...) from package piecewiseSEM (Lefcheck, 2016) for modeling the mediation through a piecewise Structural Equation Modelling approach with d-separation.

Path models: we used the piecewise Structural Equation Modelling approach as implemented by psem(...) in package piecewiseSEM (Lefcheck, 2016) and lavaan (Rosseel, 2012).

4.2 Appendix II

Here we present some examples of responses on an AX task (such as the tone task here) where pcr fails to distinguish clearly different response patterns, but the SDT-derived discrimination and bias do.

Consider an AX task with an equal numbers of signal and non-signal items, #(A ≠ X) = #(A = X), and several types of participant response strategies as given in Table 6. It can be seen that the first 6 strategies, some of which are clearly very different, have very similar % of correct responses (50% or very close to it), showing that relaying on this measure only might hide important inter-individual variation. Second, it can be seen that for the three extreme strategies 1, 3 and 5, the % correct response is exactly 50% (as expected), but also that the sensitivity d’ and bias b are identically 0.0 and -1.0, respectively, while the non-parametric estimates completely fail for strategies 1 and 3; however, criterion location c does differentiate between these three strategies in the correct direction and strength. Slightly relaxing the extreme strategies 1 and 3 and arguably making them more realistic (by allowing a small probability of just 1% of “error”) is enough to make all five measures of sensitivity and bias informative. The 6^th strategy is also highly artificial but correctly diagnosed by all 5 estimates. Finally, the last 2 strategies, while also extreme, might reflect actual participant behavior and are correctly diagnosed by the % correct responses and by the parametric estimates (but not by the non-parametric ones, whose estimation mostly fails). Therefore, this suggests that, in general, (a) the % correct responses by itself fails to disambiguate between clearly different strategies, but that (b) the parametric estimates d’ and c, when used together, do capture such differences correctly (the non-parametric estimates should work as well in non-extreme cases).

Table S27. Some examples of reponse patterns (1^st column) for a total of 1000 items equally distributed between “signal” (A ≠ X, 500 items) and “non-signal” (A = X, 500 items) that result in very similar % correct responses (3^rd column) but that may be disambiguated by SDT-derived measures of discrimination and bias: 4^th column gives the parametric estimates d’, β and c, while the last column the non-parametric ones A’ and B’’_D, showing a dash if the estimation results in not-a-number (NaN). Please see text for details about these estimates. The 2^nd column gives the counts in the order hits:false alarms:misses:correct rejections.

Response pattern	Counts	%	d’ (β, c)	A’ (B’’D)
always respond “different”	500:500:0:0	50.0%	0.00 ( 1.00, -3.09)	- (-)
99% of the time respond “different”	495:498:5:2	49.7%	-0.29 ( 2.00, -2.43)	0.35 (-0.43)
always respond “same”	0:0:500:500	50.0%	0.00 ( 1.00, 3.09)	- (-)
99% of the time respond “same”	9:3:491:497	50.6%	0.38 ( 2.38, 2.27)	0.67 ( 0.50)
respond correctly to exactly half and wrongly to the other half	250:250:250:250	50.0%	0.00 ( 1.00, -0.00)	0.50 ( 0.00)
respond randomly throwing a fair coin (i.e., 50%:50% chance)	259:234:241:266	52.5%	0.13 ( 1.00, 0.02)	0.55 ( 0.00)
respond corectly to all items	500:0:0:500	100.0%	6.18 ( 1.00, 0.00)	1.00 (-)
respond incorectly to all items	0:500:500:0	0.0%	-6.18 ( 1.00, 0.00)	- (-)

5 Session information

CPU: Apple M3 (8 threads)

RAM (memory): 17.2 GB

R version 4.3.3 (2024-02-29)

Platform: aarch64-apple-darwin20 (64-bit)

locale: en_US.UTF-8||en_US.UTF-8||en_US.UTF-8||C||en_US.UTF-8||en_US.UTF-8

attached base packages: tools, parallel, stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: benchmarkme(v.1.0.8), mediation(v.4.5.0), sandwich(v.3.1-1), mvtnorm(v.1.3-3), DHARMa(v.0.4.7), performance(v.0.13.0), glmmTMB(v.1.1.10), lmerTest(v.3.1-3), lme4(v.1.1-36), Matrix(v.1.6-5), DiagrammeR(v.1.0.11), viridis(v.0.6.5), viridisLite(v.0.4.2), sjPlot(v.2.8.17), gplots(v.3.2.0), gridExtra(v.2.3), ggrepel(v.0.9.6), piecewiseSEM(v.2.3.0), lavaanPlot(v.0.8.1), lavaan(v.0.6-19), psycho(v.0.6.1), mokken(v.3.1.2), poLCA(v.1.6.0.1), MASS(v.7.3-60.0.1), scatterplot3d(v.0.3-44), psych(v.2.4.12), factoextra(v.1.0.7), ggplot2(v.3.5.1), reshape2(v.1.4.4), dplyr(v.1.1.4), tidyr(v.1.3.1), pander(v.0.6.5) and knitr(v.1.49)

loaded via a namespace (and not attached): RColorBrewer(v.1.1-3), rstudioapi(v.0.17.1), jsonlite(v.1.8.9), datawizard(v.1.0.0), magrittr(v.2.0.3), TH.data(v.1.1-2), estimability(v.1.5.1), farver(v.2.1.2), nloptr(v.2.1.1), rmarkdown(v.2.29), ragg(v.1.3.3), vctrs(v.0.6.5), minqa(v.1.2.8), effectsize(v.1.0.0), base64enc(v.0.1-3), rstatix(v.0.7.2), htmltools(v.0.5.8.1), forcats(v.1.0.0), curl(v.6.1.0), haven(v.2.5.4), broom(v.1.0.7), Formula(v.1.2-5), sjmisc(v.2.8.10), sass(v.0.4.9), KernSmooth(v.2.23-26), bslib(v.0.8.0), DiagrammeRsvg(v.0.1), htmlwidgets(v.1.6.4), plyr(v.1.8.9), emmeans(v.1.10.6), zoo(v.1.8-12), cachem(v.1.1.0), TMB(v.1.9.16), igraph(v.2.1.2), iterators(v.1.0.14), lifecycle(v.1.0.4), pkgconfig(v.2.0.3), sjlabelled(v.1.2.0), R6(v.2.5.1), fastmap(v.1.2.0), rbibutils(v.2.3), digest(v.0.6.37), numDeriv(v.2016.8-1.1), rsvg(v.2.6.1), colorspace(v.2.1-1), textshaping(v.0.4.1), Hmisc(v.5.2-2), ggpubr(v.0.6.0), labeling(v.0.4.3), httr(v.1.4.7), abind(v.1.4-8), mgcv(v.1.9-1), compiler(v.4.3.3), doParallel(v.1.0.17), withr(v.3.0.2), htmlTable(v.2.4.3), backports(v.1.5.0), carData(v.3.0-5), ggsignif(v.0.6.4), sjstats(v.0.19.0), gtools(v.3.9.5), caTools(v.1.18.3), pbivnorm(v.0.6.0), foreign(v.0.8-88), nnet(v.7.3-20), glue(v.1.8.0), quadprog(v.1.5-8), nlme(v.3.1-166), grid(v.4.3.3), checkmate(v.2.3.2), cluster(v.2.1.8), generics(v.0.1.3), lpSolve(v.5.6.23), gtable(v.0.3.6), data.table(v.1.16.4), hms(v.1.1.3), car(v.3.1-3), foreach(v.1.5.2), pillar(v.1.10.1), stringr(v.1.5.1), benchmarkmeData(v.1.0.4), splines(v.4.3.3), lattice(v.0.22-6), survival(v.3.8-3), tidyselect(v.1.2.1), reformulas(v.0.4.0), V8(v.6.0.0), stats4(v.4.3.3), xfun(v.0.50), MuMIn(v.1.48.4), visNetwork(v.2.1.2), stringi(v.1.8.4), yaml(v.2.3.10), boot(v.1.3-31), evaluate(v.1.0.3), codetools(v.0.2-20), tibble(v.3.2.1), cli(v.3.6.3), rpart(v.4.1.24), parameters(v.0.24.1), xtable(v.1.8-4), systemfonts(v.1.1.0), Rdpack(v.2.6.2), munsell(v.0.5.1), jquerylib(v.0.1.4), Rcpp(v.1.0.14), ggeffects(v.2.0.0), coda(v.0.19-4.1), bayestestR(v.0.15.0), bitops(v.1.0-9), scales(v.1.3.0), insight(v.1.0.1), purrr(v.1.0.2), rlang(v.1.1.4), multcomp(v.1.4-26) and mnormt(v.2.1.1)

References

Ark, L. A. van der. (2007). Mokken Scale Analysis in R. Journal of Statistical Software, 20, 1–19. https://doi.org/10.18637/jss.v020.i11

Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01

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Dima, A. L. (2018). Scale validation in applied health research: Tutorial for a 6-step R-based psychometrics protocol. Health Psychology and Behavioral Medicine, 6(1), 136–161. https://doi.org/10.1080/21642850.2018.1472602

Donohue, C., & Wu, M. (2013). F0 and aspiration in Kam: Caught in the beginning. Proceedings of the International Conference on Phonetics of the Languages in China (ICPLC-13). Retrieved from https://www.researchgate.net/publication/341321803_F0_and_aspiration_in_Kam_Caught_in_the_beginning

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Wong, P. C. M., Kang, X., Wong, K. H. Y., So, H.-C., Choy, K. W., & Geng, X. (2020). ASPM-lexical tone association in speakers of a tone language: Direct evidence for the genetic-biasing hypothesis of language evolution. Science Advances, 6(22), eaba5090. https://doi.org/10.1126/sciadv.aba5090

Wu, M. (2018). A Grammar of Sanjiang Kam. München, Germany: LINCOM Gmbh.

Yip, M. (2002). Tone. Cambridge University Press, UK.

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1. This is an example note. Click the symbol at the end of the note to go back to where the note is called in the text.↩︎

2. Please note that some of the models used here are computationally expensive, and even compiling this Rmarkdown script might require a relatively powerful machine. To help with this, and to ensure full replicability of our results, we have cached some of these expensive sections in the cached_results folder as XZ-compressed RData files. However, it might happen that versions of some of the packages different from those that we used here might not be fully compatible with the saved RData files, resulting in errors compiling this Rmarkdown script or errors displaying/plotting the results. In this case, we recommend using the exact same versions of R and of the packages that we used (listed in the Session information), or, if not possible, the deletion of the offending RData files and the full recompilation of the Rmarkdown script (which is smart enough to re-generate only those missing cached results).↩︎